diff --git a/cmake/OpenCVDetectInferenceEngine.cmake b/cmake/OpenCVDetectInferenceEngine.cmake
index d82200cf01..319fd5bf0a 100644
--- a/cmake/OpenCVDetectInferenceEngine.cmake
+++ b/cmake/OpenCVDetectInferenceEngine.cmake
@@ -29,84 +29,10 @@ endif()
 
 # ======================
 
-macro(ocv_ie_find_extra_libraries find_prefix find_suffix)
-  file(GLOB libraries "${INF_ENGINE_LIB_DIRS}/${find_prefix}inference_engine*${find_suffix}")
-  foreach(full_path IN LISTS libraries)
-    get_filename_component(library "${full_path}" NAME_WE)
-    string(REPLACE "${find_prefix}" "" library "${library}")
-    if(library STREQUAL "inference_engine" OR library STREQUAL "inference_engined")
-      # skip
-    else()
-      add_library(${library} UNKNOWN IMPORTED)
-      set_target_properties(${library} PROPERTIES
-          IMPORTED_LOCATION "${full_path}")
-      list(APPEND custom_libraries ${library})
-    endif()
-  endforeach()
-endmacro()
-
-function(add_custom_ie_build _inc _lib _lib_rel _lib_dbg _msg)
-  if(NOT _inc OR NOT (_lib OR _lib_rel OR _lib_dbg))
-    return()
-  endif()
-  if(NOT _lib)
-    if(_lib_rel)
-      set(_lib "${_lib_rel}")
-    else()
-      set(_lib "${_lib_dbg}")
-    endif()
-  endif()
-  add_library(inference_engine UNKNOWN IMPORTED)
-  set_target_properties(inference_engine PROPERTIES
-    IMPORTED_LOCATION "${_lib}"
-    IMPORTED_IMPLIB_RELEASE "${_lib_rel}"
-    IMPORTED_IMPLIB_DEBUG "${_lib_dbg}"
-    INTERFACE_INCLUDE_DIRECTORIES "${_inc}"
-  )
-
-  set(custom_libraries "")
-  set(__prefixes "${CMAKE_FIND_LIBRARY_PREFIXES}")
-  if(NOT __prefixes)
-    set(__prefixes "_empty_")
-  endif()
-  foreach(find_prefix ${__prefixes})
-    if(find_prefix STREQUAL "_empty_")  # foreach doesn't iterate over empty elements
-      set(find_prefix "")
-    endif()
-    if(NOT DEFINED INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES)  # allow custom override
-      set(INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES ${CMAKE_FIND_LIBRARY_SUFFIXES})
-      if(APPLE)
-        ocv_list_filterout(INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES "^.so$")  # skip plugins (can't be linked)
-      endif()
-    endif()
-    foreach(find_suffix ${INFERENCE_ENGINE_FIND_LIBRARY_SUFFIXES})
-      ocv_ie_find_extra_libraries("${find_prefix}" "${find_suffix}")
-    endforeach()
-    if(NOT CMAKE_FIND_LIBRARY_SUFFIXES)
-      ocv_ie_find_extra_libraries("${find_prefix}" "")
-    endif()
-  endforeach()
-
-  if(NOT INF_ENGINE_RELEASE VERSION_GREATER "2018050000")
-    find_library(INF_ENGINE_OMP_LIBRARY iomp5 PATHS "${INF_ENGINE_OMP_DIR}" NO_DEFAULT_PATH)
-    if(NOT INF_ENGINE_OMP_LIBRARY)
-      message(WARNING "OpenMP for IE have not been found. Set INF_ENGINE_OMP_DIR variable if you experience build errors.")
-    endif()
-  endif()
-  if(EXISTS "${INF_ENGINE_OMP_LIBRARY}")
-    set_target_properties(inference_engine PROPERTIES IMPORTED_LINK_INTERFACE_LIBRARIES "${INF_ENGINE_OMP_LIBRARY}")
-  endif()
-  set(INF_ENGINE_VERSION "Unknown" CACHE STRING "")
-  set(INF_ENGINE_TARGET "inference_engine;${custom_libraries}" PARENT_SCOPE)
-  message(STATUS "Detected InferenceEngine: ${_msg}")
-endfunction()
-
-# ======================
-
 find_package(InferenceEngine QUIET)
 if(InferenceEngine_FOUND)
   set(INF_ENGINE_TARGET ${InferenceEngine_LIBRARIES})
-  set(INF_ENGINE_VERSION "${InferenceEngine_VERSION}" CACHE STRING "")
+  set(INF_ENGINE_VERSION "${InferenceEngine_VERSION}")
   message(STATUS "Detected InferenceEngine: cmake package (${InferenceEngine_VERSION})")
 endif()
 
@@ -124,38 +50,6 @@ elseif(DEFINED INF_ENGINE_RELEASE)
 endif()
 set(INF_ENGINE_RELEASE "${INF_ENGINE_RELEASE_INIT}" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)")
 
-if(NOT INF_ENGINE_TARGET AND INF_ENGINE_LIB_DIRS AND INF_ENGINE_INCLUDE_DIRS)
-  find_path(ie_custom_inc "inference_engine.hpp" PATHS "${INF_ENGINE_INCLUDE_DIRS}" NO_DEFAULT_PATH)
-  if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-    find_library(ie_custom_lib_dbg "inference_engined" PATHS "${INF_ENGINE_LIB_DIRS}" NO_DEFAULT_PATH)  # Win32 and MacOSX
-  endif()
-  find_library(ie_custom_lib "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}" NO_DEFAULT_PATH)
-  find_library(ie_custom_lib_rel "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}/Release" NO_DEFAULT_PATH)
-  find_library(ie_custom_lib_dbg "inference_engine" PATHS "${INF_ENGINE_LIB_DIRS}/Debug" NO_DEFAULT_PATH)
-  add_custom_ie_build("${ie_custom_inc}" "${ie_custom_lib}" "${ie_custom_lib_rel}" "${ie_custom_lib_dbg}" "INF_ENGINE_{INCLUDE,LIB}_DIRS")
-endif()
-
-set(_loc "$ENV{INTEL_OPENVINO_DIR}")
-if(NOT _loc AND DEFINED ENV{INTEL_CVSDK_DIR})
-  set(_loc "$ENV{INTEL_CVSDK_DIR}")  # OpenVINO 2018.x
-endif()
-if(NOT INF_ENGINE_TARGET AND _loc)
-  if(NOT INF_ENGINE_RELEASE VERSION_GREATER "2018050000")
-    set(INF_ENGINE_PLATFORM_DEFAULT "ubuntu_16.04")
-  else()
-    set(INF_ENGINE_PLATFORM_DEFAULT "")
-  endif()
-  set(INF_ENGINE_PLATFORM "${INF_ENGINE_PLATFORM_DEFAULT}" CACHE STRING "InferenceEngine platform (library dir)")
-  find_path(ie_custom_env_inc "inference_engine.hpp" PATHS "${_loc}/deployment_tools/inference_engine/include" NO_DEFAULT_PATH)
-  if(CMAKE_BUILD_TYPE STREQUAL "Debug")
-    find_library(ie_custom_env_lib_dbg "inference_engined" PATHS "${_loc}/deployment_tools/inference_engine/lib/${INF_ENGINE_PLATFORM}/intel64" NO_DEFAULT_PATH)
-  endif()
-  find_library(ie_custom_env_lib "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/${INF_ENGINE_PLATFORM}/intel64" NO_DEFAULT_PATH)
-  find_library(ie_custom_env_lib_rel "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/intel64/Release" NO_DEFAULT_PATH)
-  find_library(ie_custom_env_lib_dbg "inference_engine" PATHS "${_loc}/deployment_tools/inference_engine/lib/intel64/Debug" NO_DEFAULT_PATH)
-  add_custom_ie_build("${ie_custom_env_inc}" "${ie_custom_env_lib}" "${ie_custom_env_lib_rel}" "${ie_custom_env_lib_dbg}" "OpenVINO (${_loc})")
-endif()
-
 set(tgts)
 set(defs)
 
diff --git a/modules/core/include/opencv2/core/persistence.hpp b/modules/core/include/opencv2/core/persistence.hpp
index 276f640323..8e135d1a11 100644
--- a/modules/core/include/opencv2/core/persistence.hpp
+++ b/modules/core/include/opencv2/core/persistence.hpp
@@ -309,8 +309,8 @@ public:
         READ        = 0, //!< value, open the file for reading
         WRITE       = 1, //!< value, open the file for writing
         APPEND      = 2, //!< value, open the file for appending
-        MEMORY      = 4, //!< flag, read data from source or write data to the internal buffer (which is
-        //!< returned by FileStorage::release)
+        MEMORY      = 4, /**< flag, read data from source or write data to the internal buffer (which is
+                              returned by FileStorage::release) */
         FORMAT_MASK = (7<<3), //!< mask for format flags
         FORMAT_AUTO = 0,      //!< flag, auto format
         FORMAT_XML  = (1<<3), //!< flag, XML format
diff --git a/modules/core/src/hal_internal.cpp b/modules/core/src/hal_internal.cpp
index 8b74a35361..5239acc585 100644
--- a/modules/core/src/hal_internal.cpp
+++ b/modules/core/src/hal_internal.cpp
@@ -64,6 +64,16 @@
 #define HAL_LU_SMALL_MATRIX_THRESH 100
 #define HAL_CHOLESKY_SMALL_MATRIX_THRESH 100
 
+#if defined(__clang__) && defined(__has_feature)
+#if __has_feature(memory_sanitizer)
+#define CV_ANNOTATE_MEMORY_IS_INITIALIZED(address, size) \
+__msan_unpoison(adresse, size)
+#endif
+#endif
+#ifndef CV_ANNOTATE_MEMORY_IS_INITIALIZED
+#define CV_ANNOTATE_MEMORY_IS_INITIALIZED(address, size) do { } while(0)
+#endif
+
 //lapack stores matrices in column-major order so transposing is needed everywhere
 template <typename fptype> static inline void
 transpose_square_inplace(fptype *src, size_t src_ld, size_t m)
@@ -239,20 +249,16 @@ lapack_SVD(fptype* a, size_t a_step, fptype *w, fptype* u, size_t u_step, fptype
     else if(typeid(fptype) == typeid(double))
         OCV_LAPACK_FUNC(dgesdd)(mode, &m, &n, (double*)a, &lda, (double*)w, (double*)u, &ldu, (double*)vt, &ldv, (double*)buffer, &lwork, iworkBuf, info);
 
-#if defined(__clang__) && defined(__has_feature)
-#if __has_feature(memory_sanitizer)
     // Make sure MSAN sees the memory as having been written.
     // MSAN does not think it has been written because a different language was called.
-    __msan_unpoison(a, a_step * n);
-    __msan_unpoison(buffer, sizeof(fptype) * (lwork + 1));
+    CV_ANNOTATE_MEMORY_IS_INITIALIZED(a, a_step * n);
+    CV_ANNOTATE_MEMORY_IS_INITIALIZED(buffer, sizeof(fptype) * (lwork + 1));
     if (u)
-      __msan_unpoison(u, u_step * m);
+      CV_ANNOTATE_MEMORY_IS_INITIALIZED(u, u_step * m);
     if (vt)
-      __msan_unpoison(vt, v_step * n);
+      CV_ANNOTATE_MEMORY_IS_INITIALIZED(vt, v_step * n);
     if (w)
-      __msan_unpoison(w, sizeof(fptype) * std::min(m, n));
-#endif  // __has_feature(memory_sanitizer)
-#endif  // defined(__clang__) && defined(__has_feature)
+      CV_ANNOTATE_MEMORY_IS_INITIALIZED(w, sizeof(fptype) * std::min(m, n));
 
     if(!(flags & CV_HAL_SVD_NO_UV))
         transpose_square_inplace(vt, ldv, n);
@@ -357,6 +363,7 @@ lapack_QR(fptype* a, size_t a_step, int m, int n, int k, fptype* b, size_t b_ste
             dgeqrf_(&m, &n, (double*)tmpA, &ldtmpA, (double*)dst, (double*)buffer, &lwork, info);
     }
 
+    CV_ANNOTATE_MEMORY_IS_INITIALIZED(info, sizeof(int));
     if (m == n)
         transpose_square_inplace(a, lda, m);
     else
diff --git a/modules/dnn/CMakeLists.txt b/modules/dnn/CMakeLists.txt
index cc29d092f4..a9540f1088 100644
--- a/modules/dnn/CMakeLists.txt
+++ b/modules/dnn/CMakeLists.txt
@@ -165,7 +165,12 @@ elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
 endif()
 
 set(dnn_runtime_libs "")
-if(TARGET ocv.3rdparty.openvino)
+
+ocv_option(OPENCV_DNN_OPENVINO "Build with OpenVINO support (2021.4+)" (TARGET ocv.3rdparty.openvino))
+if(TARGET ocv.3rdparty.openvino AND OPENCV_DNN_OPENVINO)
+  if(NOT HAVE_OPENVINO AND NOT HAVE_NGRAPH)
+    message(FATAL_ERROR "DNN: Inference Engine is not supported without enabled 'nGraph'. Check build configuration.")
+  endif()
   list(APPEND dnn_runtime_libs ocv.3rdparty.openvino)
 endif()
 
diff --git a/modules/dnn/include/opencv2/dnn/dnn.hpp b/modules/dnn/include/opencv2/dnn/dnn.hpp
index e5e8186fa8..97033a313e 100644
--- a/modules/dnn/include/opencv2/dnn/dnn.hpp
+++ b/modules/dnn/include/opencv2/dnn/dnn.hpp
@@ -302,8 +302,6 @@ CV__DNN_INLINE_NS_BEGIN
          */
         virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &inputs);
 
-        virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs);
-
         virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs, const std::vector<Ptr<BackendNode> >& nodes);
 
         virtual Ptr<BackendNode> initVkCom(const std::vector<Ptr<BackendWrapper> > &inputs);
diff --git a/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp b/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
index 333b1bfdd2..b81806ed5a 100644
--- a/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
+++ b/modules/dnn/include/opencv2/dnn/utils/inference_engine.hpp
@@ -15,14 +15,18 @@ CV__DNN_INLINE_NS_BEGIN
 
 
 /* Values for 'OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE' parameter */
+/// @deprecated
 #define CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API     "NN_BUILDER"
+/// @deprecated
 #define CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH             "NGRAPH"
 
 /** @brief Returns Inference Engine internal backend API.
  *
  * See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros.
  *
- * Default value is controlled through `OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE` runtime parameter (environment variable).
+ * `OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE` runtime parameter (environment variable) is ignored since 4.6.0.
+ *
+ * @deprecated
  */
 CV_EXPORTS_W cv::String getInferenceEngineBackendType();
 
@@ -31,6 +35,8 @@ CV_EXPORTS_W cv::String getInferenceEngineBackendType();
  * See values of `CV_DNN_BACKEND_INFERENCE_ENGINE_*` macros.
  *
  * @returns previous value of internal backend API
+ *
+ * @deprecated
  */
 CV_EXPORTS_W cv::String setInferenceEngineBackendType(const cv::String& newBackendType);
 
diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp
index e431296154..954ada50f2 100644
--- a/modules/dnn/src/dnn.cpp
+++ b/modules/dnn/src/dnn.cpp
@@ -177,48 +177,29 @@ private:
 
 #ifdef HAVE_INF_ENGINE
         if (checkIETarget(DNN_TARGET_CPU)) {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-            backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_CPU));
-#endif
 #ifdef HAVE_DNN_NGRAPH
             backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_CPU));
 #endif
         }
         if (checkIETarget(DNN_TARGET_MYRIAD)) {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-            backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_MYRIAD));
-#endif
 #ifdef HAVE_DNN_NGRAPH
             backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_MYRIAD));
 #endif
         }
         if (checkIETarget(DNN_TARGET_HDDL)) {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-            backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_HDDL));
-#endif
 #ifdef HAVE_DNN_NGRAPH
             backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_HDDL));
 #endif
         }
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-        if (checkIETarget(DNN_TARGET_FPGA))
-            backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_FPGA));
-#endif
 #ifdef HAVE_OPENCL
         if (cv::ocl::useOpenCL() && ocl::Device::getDefault().isIntel())
         {
             if (checkIETarget(DNN_TARGET_OPENCL)) {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-                backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL));
-#endif
 #ifdef HAVE_DNN_NGRAPH
                 backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL));
 #endif
             }
             if (checkIETarget(DNN_TARGET_OPENCL_FP16)) {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-                backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL_FP16));
-#endif
 #ifdef HAVE_DNN_NGRAPH
                 backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL_FP16));
 #endif
@@ -273,7 +254,7 @@ std::vector<Target> getAvailableTargets(Backend be)
         be = (Backend)PARAM_DNN_BACKEND_DEFAULT;
 #ifdef HAVE_INF_ENGINE
     if (be == DNN_BACKEND_INFERENCE_ENGINE)
-        be = getInferenceEngineBackendTypeParam();
+        be = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
 #endif
 
     std::vector<Target> result;
@@ -637,8 +618,7 @@ struct DataLayer : public Layer
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-        return backendId == DNN_BACKEND_OPENCV ||
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && inputsData.size() == 1);
+        return backendId == DNN_BACKEND_OPENCV;
     }
 
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
@@ -829,39 +809,6 @@ struct DataLayer : public Layer
         }
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        CV_CheckEQ(inputsData.size(), (size_t)1, "");
-        CV_CheckEQ(inputsData[0].dims, 4, "");
-        const size_t numChannels = inputsData[0].size[1];
-        CV_Assert(numChannels <= 4);
-
-        // Scale
-        InferenceEngine::TensorDesc td(InferenceEngine::Precision::FP32, {numChannels},
-                                       InferenceEngine::Layout::C);
-        auto weights = InferenceEngine::make_shared_blob<float>(td);
-        weights->allocate();
-
-        float* weight_buf = weights->buffer().as<float*>();
-        std::fill(weight_buf, weight_buf + numChannels, scaleFactors[0]);
-
-        // Mean subtraction
-        auto biases = InferenceEngine::make_shared_blob<float>(td);
-        biases->allocate();
-        float* bias_buf = biases->buffer().as<float*>();
-
-        for (int i = 0; i < numChannels; ++i)
-        {
-            bias_buf[i] = -means[0][i] * scaleFactors[0];
-        }
-
-        InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name);
-        addConstantData("weights", weights, ieLayer);
-        addConstantData("biases", biases, ieLayer);
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
     std::vector<String> outNames;
     std::vector<MatShape> shapes;
@@ -1119,18 +1066,14 @@ static Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
     }
     else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
     {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-        return Ptr<BackendWrapper>(new InfEngineBackendWrapper(targetId, m));
-#else
-        CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
-#endif
+        CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019;
     }
     else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
     {
 #ifdef HAVE_DNN_NGRAPH
         return Ptr<BackendWrapper>(new NgraphBackendWrapper(targetId, m));
 #else
-        CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Inference Engine + nGraph");
+        CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of OpenVINO / Inference Engine + nGraph");
 #endif
     }
     else if (backendId == DNN_BACKEND_WEBNN)
@@ -1278,7 +1221,7 @@ struct Net::Impl : public detail::NetImplBase
             }
             else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
             {
-                return wrapMat(preferableBackend, preferableTarget, host);
+                CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019;
             }
             else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             {
@@ -1407,7 +1350,7 @@ struct Net::Impl : public detail::NetImplBase
             preferableBackend = (Backend)PARAM_DNN_BACKEND_DEFAULT;
 #ifdef HAVE_INF_ENGINE
         if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE)
-            preferableBackend = getInferenceEngineBackendTypeParam();
+            preferableBackend = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;  // = getInferenceEngineBackendTypeParam();
 #endif
 
         CV_Assert(preferableBackend != DNN_BACKEND_OPENCV ||
@@ -1418,8 +1361,7 @@ struct Net::Impl : public detail::NetImplBase
                   preferableTarget == DNN_TARGET_CPU ||
                   preferableTarget == DNN_TARGET_OPENCL);
 #ifdef HAVE_INF_ENGINE
-        if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
-            preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
             CV_Assert(
                   (preferableTarget == DNN_TARGET_CPU && (!isArmComputePlugin() || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) ||
@@ -1721,14 +1663,6 @@ struct Net::Impl : public detail::NetImplBase
         }
         else if (preferableBackend == DNN_BACKEND_HALIDE)
             initHalideBackend();
-        else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-            initInfEngineBackend(blobsToKeep_);
-#else
-            CV_Assert(false && "This OpenCV version is built without Inference Engine NN Builder API support");
-#endif
-        }
         else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
 #ifdef HAVE_DNN_NGRAPH
@@ -1750,7 +1684,7 @@ struct Net::Impl : public detail::NetImplBase
         else if (preferableBackend == DNN_BACKEND_CUDA)
             initCUDABackend(blobsToKeep_);
         else
-            CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
+            CV_Error(Error::StsNotImplemented, cv::format("Unknown backend identifier: %d", preferableBackend));
     }
 
     void initHalideBackend()
@@ -1808,316 +1742,6 @@ struct Net::Impl : public detail::NetImplBase
         }
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    // Before launching Inference Engine graph we need to specify output blobs.
-    // This function requests output blobs based on inputs references of
-    // layers from default backend or layers from different graphs.
-    void addInfEngineNetOutputs(LayerData &ld)
-    {
-        CV_TRACE_FUNCTION();
-        Ptr<InfEngineBackendNet> layerNet;
-        if (ld.backendNodes.find(preferableBackend) != ld.backendNodes.end())
-        {
-            Ptr<BackendNode> node = ld.backendNodes[preferableBackend];
-            if (!node.empty())
-            {
-                Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
-                CV_Assert(!ieNode.empty()); CV_Assert(!ieNode->net.empty());
-                layerNet = ieNode->net;
-            }
-        }
-        // For an every input reference we check that it belongs to one of
-        // the Inference Engine backend graphs. Request an output blob if it is.
-        // Do nothing if layer's input is from the same graph.
-        for (int i = 0; i < ld.inputBlobsId.size(); ++i)
-        {
-            LayerData &inpLd = layers[ld.inputBlobsId[i].lid];
-            Ptr<BackendNode> inpNode = inpLd.backendNodes[preferableBackend];
-            if (!inpNode.empty())
-            {
-                Ptr<InfEngineBackendNode> ieInpNode = inpNode.dynamicCast<InfEngineBackendNode>();
-                CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
-                if (layerNet != ieInpNode->net)
-                {
-                    // layerNet is empty or nodes are from different graphs.
-                    ieInpNode->net->addOutput(ieInpNode->layer.getName());
-                }
-            }
-        }
-    }
-
-    void initInfEngineBackend(const std::vector<LayerPin>& blobsToKeep_)
-    {
-        CV_TRACE_FUNCTION();
-        CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, haveInfEngine());
-        MapIdToLayerData::iterator it;
-        Ptr<InfEngineBackendNet> net;
-
-        for (it = layers.begin(); it != layers.end(); ++it)
-        {
-            LayerData &ld = it->second;
-            if (ld.id == 0)
-            {
-                CV_Assert((netInputLayer->outNames.empty() && ld.outputBlobsWrappers.size() == 1) ||
-                          (netInputLayer->outNames.size() == ld.outputBlobsWrappers.size()));
-                for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
-                {
-                    InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
-                    dataPtr->name = netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i];
-#else
-                    dataPtr->setName(netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]);
-#endif
-                }
-            }
-            else
-            {
-                for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
-                {
-                    InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
-                    dataPtr->name = ld.name;
-#else
-                    dataPtr->setName(ld.name);
-#endif
-                }
-            }
-        }
-
-        if (skipInfEngineInit)
-        {
-            Ptr<BackendNode> node = layers[lastLayerId].backendNodes[preferableBackend];
-            CV_Assert(!node.empty());
-
-            Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
-            CV_Assert(!ieNode.empty());
-            ieNode->net->reset();
-
-            for (it = layers.begin(); it != layers.end(); ++it)
-            {
-                LayerData &ld = it->second;
-                if (ld.id == 0)
-                {
-                    for (int i = 0; i < ld.inputBlobsWrappers.size(); ++i)
-                    {
-                        InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.inputBlobsWrappers[i]);
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
-                        dataPtr->name = netInputLayer->outNames[i];
-#else
-                        dataPtr->setName(netInputLayer->outNames[i]);
-#endif
-                    }
-                }
-                else
-                {
-                    for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
-                    {
-                        InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
-                        dataPtr->name = ld.name;
-#else
-                        dataPtr->setName(ld.name);
-#endif
-                    }
-                }
-                ieNode->net->addBlobs(ld.inputBlobsWrappers);
-                ieNode->net->addBlobs(ld.outputBlobsWrappers);
-                ld.skip = true;
-            }
-            layers[lastLayerId].skip = false;
-            ieNode->net->init((Target)preferableTarget);
-            return;
-        }
-
-        // Build Inference Engine networks from sets of layers that support this
-        // backend. Split a whole model on several Inference Engine networks if
-        // some of layers are not implemented.
-
-        bool supportsCPUFallback = preferableTarget == DNN_TARGET_CPU ||
-                                   BackendRegistry::checkIETarget(DNN_TARGET_CPU);
-
-        // Set of all input and output blobs wrappers for current network.
-        std::map<LayerPin, Ptr<BackendWrapper> > netBlobsWrappers;
-        for (it = layers.begin(); it != layers.end(); ++it)
-        {
-            LayerData &ld = it->second;
-            if (ld.id == 0 && ld.skip)
-                continue;
-            bool fused = ld.skip;
-
-            Ptr<Layer> layer = ld.layerInstance;
-            if (!fused && !layer->supportBackend(preferableBackend))
-            {
-                bool customizable = ld.id != 0 &&
-                                    INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R2) &&
-                                    supportsCPUFallback;
-                // TODO: there is a bug in Myriad plugin with custom layers shape infer.
-                if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL)
-                {
-                    for (int i = 0; customizable && i < ld.inputBlobs.size(); ++i)
-                    {
-                        customizable = ld.inputBlobs[i]->size[0] == 1;
-                    }
-                }
-
-                // TODO: fix these workarounds
-                if (preferableTarget == DNN_TARGET_MYRIAD ||
-                    preferableTarget == DNN_TARGET_HDDL ||
-                    preferableTarget == DNN_TARGET_OPENCL ||
-                    preferableTarget == DNN_TARGET_OPENCL_FP16)
-                    customizable &= ld.type != "Concat";
-
-                if (preferableTarget == DNN_TARGET_OPENCL ||
-                    preferableTarget == DNN_TARGET_OPENCL_FP16)
-                    customizable &= ld.type != "Power";
-
-                if (preferableTarget == DNN_TARGET_OPENCL)
-                    customizable &= ld.type != "Eltwise";
-
-                if (!customizable)
-                {
-                    addInfEngineNetOutputs(ld);
-                    net = Ptr<InfEngineBackendNet>();
-                    netBlobsWrappers.clear();  // Is not used for R5 release but we don't wrap it to #ifdef.
-                    layer->preferableTarget = DNN_TARGET_CPU;
-                    continue;
-                }
-            }
-            ld.skip = true;  // Initially skip all Inference Engine supported layers.
-
-            // Create a new network if one of inputs from different Inference Engine graph.
-            for (int i = 0; i < ld.inputBlobsId.size(); ++i)
-            {
-                LayerData &inpLd = layers[ld.inputBlobsId[i].lid];
-                Ptr<BackendNode> inpNode = inpLd.backendNodes[preferableBackend];
-                if (!inpNode.empty())
-                {
-                    Ptr<InfEngineBackendNode> ieInpNode = inpNode.dynamicCast<InfEngineBackendNode>();
-                    CV_Assert(!ieInpNode.empty()); CV_Assert(!ieInpNode->net.empty());
-                    if (ieInpNode->net != net)
-                    {
-                        net = Ptr<InfEngineBackendNet>();
-                        netBlobsWrappers.clear();  // Is not used for R5 release but we don't wrap it to #ifdef.
-                        break;
-                    }
-                }
-            }
-
-            Ptr<BackendNode> node;
-            if (!net.empty())
-            {
-                if (fused)
-                {
-                    bool inPlace = ld.inputBlobsId.size() == 1 && ld.outputBlobs.size() == 1 &&
-                                   ld.inputBlobs[0]->data == ld.outputBlobs[0].data;
-                    CV_Assert(inPlace);
-                    node = layers[ld.inputBlobsId[0].lid].backendNodes[preferableBackend];
-                    ld.inputBlobsWrappers = layers[ld.inputBlobsId[0].lid].inputBlobsWrappers;
-                }
-            }
-            else
-                net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet());
-
-            if (!fused)
-            {
-                if (layer->supportBackend(preferableBackend))
-                    node = layer->initInfEngine(ld.inputBlobsWrappers);
-                else
-                {
-                    node = Ptr<BackendNode>(new InfEngineBackendNode(
-                        ld.layerInstance, ld.inputBlobs, ld.outputBlobs, ld.internals));
-                }
-            }
-            else if (node.empty())
-                continue;
-
-            CV_Assert(!node.empty());
-            ld.backendNodes[preferableBackend] = node;
-
-            Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
-            CV_Assert(!ieNode.empty());
-            ieNode->net = net;
-
-            for (const auto& pin : blobsToKeep_)
-            {
-                if (pin.lid == ld.id)
-                {
-                    ieNode->net->addOutput(ieNode->layer.getName());
-                    break;
-                }
-            }
-
-            // Convert weights in FP16 for specific targets.
-            if ((preferableTarget == DNN_TARGET_OPENCL_FP16 ||
-                 preferableTarget == DNN_TARGET_MYRIAD ||
-                 preferableTarget == DNN_TARGET_HDDL ||
-                 preferableTarget == DNN_TARGET_FPGA) && !fused)
-            {
-#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
-                for (const std::string& name : {"weights", "biases"})
-                {
-                    auto it = ieNode->layer.getParameters().find(name);
-                    if (it != ieNode->layer.getParameters().end())
-                    {
-                        InferenceEngine::Blob::Ptr bp = it->second.as<InferenceEngine::Blob::Ptr>();
-                        it->second = convertFp16(std::const_pointer_cast<InferenceEngine::Blob>(bp));
-                    }
-                }
-#else
-                auto& blobs = ieNode->layer.getConstantData();
-                if (blobs.empty())
-                {
-                    // In case of non weightable layer we have to specify
-                    // it's precision adding dummy blob.
-                    auto blob = InferenceEngine::make_shared_blob<int16_t>(
-                                    InferenceEngine::Precision::FP16,
-                                    InferenceEngine::Layout::C, {1});
-                    blob->allocate();
-                    blobs[""] = blob;
-                }
-                else
-                {
-                    for (auto& it : blobs)
-                        it.second = convertFp16(std::const_pointer_cast<InferenceEngine::Blob>(it.second));
-                }
-#endif
-            }
-
-            if (!fused)
-                net->addLayer(ieNode->layer);
-
-            net->connect(ld.inputBlobsWrappers, ld.outputBlobsWrappers, ieNode->layer.getName());
-            net->addBlobs(ld.inputBlobsWrappers);
-            net->addBlobs(ld.outputBlobsWrappers);
-            addInfEngineNetOutputs(ld);
-        }
-
-        // Initialize all networks.
-        for (MapIdToLayerData::reverse_iterator it = layers.rbegin(); it != layers.rend(); ++it)
-        {
-            LayerData &ld = it->second;
-            if (ld.backendNodes.find(preferableBackend) == ld.backendNodes.end())
-                continue;
-
-            Ptr<BackendNode> node = ld.backendNodes[preferableBackend];
-            if (node.empty())
-                continue;
-
-            Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
-            if (ieNode.empty())
-                continue;
-
-            CV_Assert(!ieNode->net.empty());
-
-            if (!ieNode->net->isInitialized())
-            {
-                ieNode->net->init((Target)preferableTarget);
-                ld.skip = false;
-            }
-        }
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 
 #ifdef HAVE_DNN_NGRAPH
     /** mark input pins as outputs from other subnetworks
@@ -2162,7 +1786,7 @@ struct Net::Impl : public detail::NetImplBase
     void initNgraphBackend(const std::vector<LayerPin>& blobsToKeep_)
     {
         CV_TRACE_FUNCTION();
-        CV_Assert_N(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, haveInfEngine());
+        CV_CheckEQ(preferableBackend, DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, "");
 
         Ptr<InfEngineNgraphNet> net;
 
@@ -2993,7 +2617,6 @@ struct Net::Impl : public detail::NetImplBase
 
         if(!fusion || (preferableBackend != DNN_BACKEND_OPENCV &&
                         preferableBackend != DNN_BACKEND_CUDA &&
-                        preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
                         preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH))
            return;
 
@@ -3814,15 +3437,13 @@ struct Net::Impl : public detail::NetImplBase
                 {
                     forwardHalide(ld.outputBlobsWrappers, node);
                 }
-                else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-                {
-                    forwardInfEngine(ld.outputBlobsWrappers, node, isAsync);
-                }
+#ifdef HAVE_INF_ENGINE
                 else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
                 {
                     forwardNgraph(ld.outputBlobsWrappers, node, isAsync);
                 }
-                 else if (preferableBackend == DNN_BACKEND_WEBNN)
+#endif
+                else if (preferableBackend == DNN_BACKEND_WEBNN)
                 {
                     forwardWebnn(ld.outputBlobsWrappers, node, isAsync);
                 }
@@ -4181,27 +3802,13 @@ struct Net::Impl : public detail::NetImplBase
             // Transfer data to CPU if it's require.
             ld.outputBlobsWrappers[pin.oid]->copyToHost();
         }
-        CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
+        CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
 
-        if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-            Ptr<InfEngineBackendWrapper> wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast<InfEngineBackendWrapper>();
-            return std::move(wrapper->futureMat);
+        Ptr<NgraphBackendWrapper> wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast<NgraphBackendWrapper>();
+        return std::move(wrapper->futureMat);
 #else
-            CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
-#endif
-        }
-        else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        {
-#ifdef HAVE_DNN_NGRAPH
-            Ptr<NgraphBackendWrapper> wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast<NgraphBackendWrapper>();
-            return std::move(wrapper->futureMat);
-#else
-            CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of Inference Engine + nGraph");
-#endif
-        }
+        CV_Error(Error::StsNotImplemented, "DNN: OpenVINO/nGraph backend is required");
 #endif  // HAVE_INF_ENGINE
-        CV_Error(Error::StsNotImplemented, "DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 backend is required");
     }
 
     AsyncArray getBlobAsync(String outputName)
@@ -4275,40 +3882,18 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
     CV_TRACE_REGION_NEXT("backendNode");
 
     Ptr<BackendNode> backendNode;
-#ifdef HAVE_DNN_NGRAPH
-    if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam())
     {
         auto fake_node = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, ngraph::Shape{});
         Ptr<InfEngineNgraphNode> backendNodeNGraph(new InfEngineNgraphNode(fake_node));
         backendNodeNGraph->net = Ptr<InfEngineNgraphNet>(new InfEngineNgraphNet(*(cvNet.impl), ieNet));
         backendNode = backendNodeNGraph;
     }
-    else
-#endif
-    {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-        Ptr<InfEngineBackendNode> backendNodeNN(new InfEngineBackendNode(InferenceEngine::Builder::Layer("")));
-        backendNodeNN->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet));
-        backendNode = backendNodeNN;
-#else
-        CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
-#endif
-    }
 
     CV_TRACE_REGION_NEXT("register_outputs");
 
-#ifdef HAVE_DNN_NGRAPH
     auto ngraphFunction = ieNet.getFunction();
-#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2)
-    std::list< std::shared_ptr<ngraph::Node> > ngraphOperations;
-#else
-    std::vector< std::shared_ptr<ngraph::Node> > ngraphOperations;
-#endif
-    if (ngraphFunction)
-    {
-        ngraphOperations = ngraphFunction->get_ops();
-    }
-#endif
+    CV_Assert(ngraphFunction);
+    std::vector< std::shared_ptr<ngraph::Node> > ngraphOperations = ngraphFunction->get_ops();
 
     for (auto& it : ieNet.getOutputsInfo())
     {
@@ -4320,8 +3905,6 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
 
         LayerData& ld = cvNet.impl->layers[lid];
 
-#ifdef HAVE_DNN_NGRAPH
-        if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam())
         {
             Ptr<Layer> cvLayer(new NgraphBackendLayer(ieNet));
             cvLayer->name = outputName;
@@ -4329,44 +3912,18 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
 
             auto process_layer = [&](const std::string& name) -> bool
             {
-                if (ngraphFunction)
-                {
-                    CV_TRACE_REGION("ngraph_function");
-                    for (const auto& op : ngraphOperations)
-                    {
-                        CV_Assert(op);
-                        if (op->get_friendly_name() == name)
-                        {
-                            const std::string typeName = op->get_type_info().name;
-                            cvLayer->type = typeName;
-                            return true;
-                        }
-                    }
-                    return false;
-                }
-                else
+                CV_TRACE_REGION("ngraph_function");
+                for (const auto& op : ngraphOperations)
                 {
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
-                    CV_Error(Error::StsNotImplemented, "This OpenCV version is built with Inference Engine which has dropped IR v7 support");
-#else
-                    CV_TRACE_REGION("legacy_cnn_layer");
-                    try
+                    CV_Assert(op);
+                    if (op->get_friendly_name() == name)
                     {
-                        InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(name.c_str());
-                        CV_Assert(ieLayer);
-
-                        cvLayer->type = ieLayer->type;
+                        const std::string typeName = op->get_type_info().name;
+                        cvLayer->type = typeName;
                         return true;
                     }
-                    catch (const std::exception& e)
-                    {
-                        CV_UNUSED(e);
-                        CV_LOG_DEBUG(NULL, "IE layer extraction failure: '" << name << "' - " << e.what());
-                        return false;
-                    }
-#endif
-
                 }
+                return false;
             };
 
             bool found = process_layer(outputName);
@@ -4385,37 +3942,6 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
             ld.layerInstance = cvLayer;
             ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NGRAPH] = backendNode;
         }
-        else
-#endif
-        {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-            Ptr<Layer> cvLayer(new InfEngineBackendLayer(ieNet));
-
-            InferenceEngine::CNNLayerPtr ieLayer;
-            try
-            {
-                ieLayer = ieNet.getLayerByName(outputName.c_str());
-            }
-            catch (...)
-            {
-                auto pos = outputName.rfind('.');  // cut port number: ".0"
-                if (pos != std::string::npos)
-                {
-                    std::string layerName = outputName.substr(0, pos);
-                    ieLayer = ieNet.getLayerByName(layerName.c_str());
-                }
-            }
-            CV_Assert(ieLayer);
-
-            cvLayer->name = outputName;
-            cvLayer->type = ieLayer->type;
-            ld.layerInstance = cvLayer;
-
-            ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019] = backendNode;
-#else
-            CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
-#endif
-        }
 
         for (int i = 0; i < inputsNames.size(); ++i)
             cvNet.connect(0, i, lid, i);
@@ -4423,7 +3949,7 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
 
     CV_TRACE_REGION_NEXT("finalize");
 
-    cvNet.setPreferableBackend(getInferenceEngineBackendTypeParam());
+    cvNet.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
 
     cvNet.impl->skipInfEngineInit = true;
     return cvNet;
@@ -4440,16 +3966,8 @@ Net Net::readFromModelOptimizer(const String& xml, const String& bin)
 
     FPDenormalsIgnoreHintScope fp_denormals_ignore_scope;
 
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
-    InferenceEngine::CNNNetReader reader;
-    reader.ReadNetwork(xml);
-    reader.ReadWeights(bin);
-
-    InferenceEngine::CNNNetwork ieNet = reader.getNetwork();
-#else
     InferenceEngine::Core& ie = getCore("");
     InferenceEngine::CNNNetwork ieNet = ie.ReadNetwork(xml, bin);
-#endif
 
     return Impl::createNetworkFromModelOptimizer(ieNet);
 #endif  // HAVE_INF_ENGINE
@@ -4478,26 +3996,6 @@ Net Net::readFromModelOptimizer(
 
     FPDenormalsIgnoreHintScope fp_denormals_ignore_scope;
 
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
-    InferenceEngine::CNNNetReader reader;
-
-    try
-    {
-        reader.ReadNetwork(bufferModelConfigPtr, bufferModelConfigSize);
-
-        InferenceEngine::TensorDesc tensorDesc(InferenceEngine::Precision::U8, { bufferWeightsSize }, InferenceEngine::Layout::C);
-        InferenceEngine::TBlob<uint8_t>::Ptr weightsBlobPtr(new InferenceEngine::TBlob<uint8_t>(tensorDesc));
-        weightsBlobPtr->allocate();
-        std::memcpy(weightsBlobPtr->buffer(), (uchar*)bufferWeightsPtr, bufferWeightsSize);
-        reader.SetWeights(weightsBlobPtr);
-    }
-    catch (const std::exception& e)
-    {
-        CV_Error(Error::StsError, std::string("DNN: IE failed to load model: ") + e.what());
-    }
-
-    InferenceEngine::CNNNetwork ieNet = reader.getNetwork();
-#else
     InferenceEngine::Core& ie = getCore("");
 
     std::string model; model.assign((char*)bufferModelConfigPtr, bufferModelConfigSize);
@@ -4514,7 +4012,6 @@ Net Net::readFromModelOptimizer(
     {
         CV_Error(Error::StsError, std::string("DNN: IE failed to load model: ") + e.what());
     }
-#endif
 
     return Impl::createNetworkFromModelOptimizer(ieNet);
 #endif  // HAVE_INF_ENGINE
@@ -4621,8 +4118,8 @@ AsyncArray Net::forwardAsync(const String& outputName)
     std::vector<LayerPin> pins(1, impl->getPinByAlias(layerName));
     impl->setUpNet(pins);
 
-    if (!(impl->preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || impl->preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH))
-        CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward is supported for Inference Engine backends only");
+    if (impl->preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        CV_Error(Error::StsNotImplemented, "DNN: Asynchronous forward is supported for Inference Engine backend only");
 
     impl->isAsync = true;
     impl->forwardToLayer(impl->getLayerData(layerName));
@@ -5047,7 +4544,7 @@ void Net::setPreferableBackend(int backendId)
 
 #ifdef HAVE_INF_ENGINE
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
-        backendId = getInferenceEngineBackendTypeParam();
+        backendId = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
 #endif
 
     if( impl->preferableBackend != backendId )
@@ -5292,8 +4789,8 @@ string Net::Impl::dump() const
         case DNN_BACKEND_DEFAULT: backend = "DEFAULT/"; break;
         case DNN_BACKEND_HALIDE: backend = "HALIDE/"; break;
         case DNN_BACKEND_INFERENCE_ENGINE: // fallthru
-        case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019: backend = "DLIE/"; break;
-        case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: backend = "NGRAPH/"; break;
+        case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019: // fallthru
+        case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: backend = "OpenVINO/"; break;
         case DNN_BACKEND_OPENCV: backend = "OCV/"; break;
         case DNN_BACKEND_VKCOM: backend = "VULKAN/"; break;
         case DNN_BACKEND_CUDA: backend = "CUDA/"; break;
@@ -5894,13 +5391,6 @@ Ptr<BackendNode> Layer::initHalide(const std::vector<Ptr<BackendWrapper> > &)
     return Ptr<BackendNode>();
 }
 
-Ptr<BackendNode> Layer::initInfEngine(const std::vector<Ptr<BackendWrapper> > &)
-{
-    CV_Error(Error::StsNotImplemented, "Inference Engine pipeline of " + type +
-                                       " layers is not defined.");
-    return Ptr<BackendNode>();
-}
-
 Ptr<BackendNode> Layer::initNgraph(const std::vector<Ptr<BackendWrapper> > & inputs, const std::vector<Ptr<BackendNode> >& nodes)
 {
     CV_Error(Error::StsNotImplemented, "Inference Engine pipeline of " + type +
diff --git a/modules/dnn/src/layers/batch_norm_layer.cpp b/modules/dnn/src/layers/batch_norm_layer.cpp
index d22a070805..bb81f14425 100644
--- a/modules/dnn/src/layers/batch_norm_layer.cpp
+++ b/modules/dnn/src/layers/batch_norm_layer.cpp
@@ -170,11 +170,14 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return preferableTarget == DNN_TARGET_CPU || dims == 4;
+#endif
         return (backendId == DNN_BACKEND_OPENCV) ||
                backendId == DNN_BACKEND_CUDA ||
                (backendId == DNN_BACKEND_HALIDE && haveHalide()) ||
-               backendId == DNN_BACKEND_WEBNN ||
-               ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine() && (preferableTarget == DNN_TARGET_CPU || dims == 4));
+               backendId == DNN_BACKEND_WEBNN;
     }
 
 #ifdef HAVE_OPENCL
@@ -382,16 +385,6 @@ public:
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name);
-        const size_t numChannels = weights_.total();
-        addConstantData("weights", wrapToInfEngineBlob(weights_, {numChannels}, InferenceEngine::Layout::C), ieLayer);
-        addConstantData("biases", wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C), ieLayer);
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/blank_layer.cpp b/modules/dnn/src/layers/blank_layer.cpp
index 59548a9c0c..0d6ab19e4d 100644
--- a/modules/dnn/src/layers/blank_layer.cpp
+++ b/modules/dnn/src/layers/blank_layer.cpp
@@ -63,9 +63,12 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_CUDA ||
-               ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
+               backendId == DNN_BACKEND_CUDA;
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -116,32 +119,6 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
-    {
-        InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
-        std::vector<size_t> dims = input->getDims();
-        CV_Assert(!dims.empty());
-
-        InferenceEngine::Builder::Layer ieLayer(name);
-        ieLayer.setName(name);
-        if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL)
-        {
-            ieLayer.setType("Copy");
-        }
-        else
-        {
-            ieLayer.setType("Split");
-            ieLayer.getParameters()["axis"] = dims.size() - 1;
-            ieLayer.getParameters()["out_sizes"] = dims[0];
-        }
-        ieLayer.setInputPorts({InferenceEngine::Port(dims)});
-        ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
-
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/concat_layer.cpp b/modules/dnn/src/layers/concat_layer.cpp
index f620d66a39..675546f76f 100644
--- a/modules/dnn/src/layers/concat_layer.cpp
+++ b/modules/dnn/src/layers/concat_layer.cpp
@@ -113,11 +113,13 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                (backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !padding) ||  // By channels
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() && !padding) ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
                (backendId == DNN_BACKEND_WEBNN && !padding) ||
                (backendId == DNN_BACKEND_VKCOM && haveVulkan() && !padding);
     }
@@ -343,18 +345,6 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
-    {
-        InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
-
-        InferenceEngine::Builder::ConcatLayer ieLayer(name);
-        ieLayer.setAxis(normalize_axis(axis, input->getDims().size()));
-        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
diff --git a/modules/dnn/src/layers/const_layer.cpp b/modules/dnn/src/layers/const_layer.cpp
index 1f307b8fa6..4392763be7 100644
--- a/modules/dnn/src/layers/const_layer.cpp
+++ b/modules/dnn/src/layers/const_layer.cpp
@@ -34,9 +34,11 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
                backendId == DNN_BACKEND_WEBNN ||
                backendId == DNN_BACKEND_CUDA;
     }
@@ -78,16 +80,6 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::ConstLayer ieLayer(name);
-        ieLayer.setData(wrapToInfEngineBlob(blobs[0]));
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
-
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp
index df673fe864..1af34472df 100644
--- a/modules/dnn/src/layers/convolution_layer.cpp
+++ b/modules/dnn/src/layers/convolution_layer.cpp
@@ -330,7 +330,7 @@ public:
         }
 #endif
 #ifdef HAVE_INF_ENGINE
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
             bool isArmTarget = preferableTarget == DNN_TARGET_CPU && isArmComputePlugin();
             if (isArmTarget && blobs.empty())
@@ -340,7 +340,7 @@ public:
             if (ksize == 3)
                 return preferableTarget != DNN_TARGET_MYRIAD && !isArmTarget;
             bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
-            if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || !isMyriad) && blobs.empty())
+            if (!isMyriad && blobs.empty())
                 return false;
             return (!isMyriad || dilation.width == dilation.height);
         }
@@ -761,69 +761,6 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
-    {
-        CV_Assert(!blobs.empty());
-        InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
-        std::vector<size_t> dims = input->getDims();
-        CV_Assert(dims.size() == 4 || dims.size() == 5);
-        const int inpCn = dims[1];
-        const int outCn = blobs[0].size[0];
-        const int inpGroupCn = blobs[0].size[1];
-        const int group = inpCn / inpGroupCn;
-        InferenceEngine::Layout layout = (dims.size() == 4) ? InferenceEngine::Layout::OIHW :
-                                                              InferenceEngine::Layout::NCDHW;
-
-        auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
-        if (fusedWeights)
-        {
-            if (weightsMat.isContinuous())
-            {
-                Mat cvWeights = weightsMat.reshape(1, blobs[0].dims, blobs[0].size);
-                ieWeights = wrapToInfEngineBlob(cvWeights, layout);
-            }
-            else
-            {
-                ieWeights = InferenceEngine::make_shared_blob<float>({
-                                InferenceEngine::Precision::FP32,
-                                ieWeights->getTensorDesc().getDims(), layout
-                            });
-                ieWeights->allocate();
-
-                Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, outCn);
-                Mat cvWeights = weightsMat.colRange(0, newWeights.cols);
-                cvWeights.copyTo(newWeights);
-            }
-        }
-        InferenceEngine::Blob::Ptr ieBiases;
-        if (hasBias() || fusedBias)
-        {
-            Mat biasesMat({outCn}, CV_32F, &biasvec[0]);
-            ieBiases = wrapToInfEngineBlob(biasesMat, {(size_t)outCn}, InferenceEngine::Layout::C);
-        }
-
-        InferenceEngine::Builder::ConvolutionLayer ieLayer(name);
-
-        ieLayer.setKernel(kernel_size);
-        ieLayer.setStrides(strides);
-        ieLayer.setDilation(dilations);
-        ieLayer.setPaddingsBegin(pads_begin);
-        ieLayer.setPaddingsEnd(pads_end);
-        ieLayer.setGroup((size_t)group);
-        ieLayer.setOutDepth((size_t)outCn);
-
-        InferenceEngine::Builder::Layer l = ieLayer;
-        addConstantData("weights", ieWeights, l);
-        if (ieBiases)
-            addConstantData("biases", ieBiases, l);
-
-        if (!padMode.empty())
-            l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper");
-
-        return Ptr<BackendNode>(new InfEngineBackendNode(l));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
@@ -2319,52 +2256,6 @@ public:
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
             return group == 1;
         }
-
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        {
-            if (kernel_size.size() == 3 && preferableTarget != DNN_TARGET_CPU) {
-                return false;
-            }
-
-            if (std::accumulate(adjust_pads.begin(), adjust_pads.end(), 0, std::plus<size_t>()) > 0)
-            {
-                if (padMode.empty())
-                {
-                    if (preferableTarget != DNN_TARGET_CPU && group != 1)
-                    {
-                        for (int i = 0; i < adjust_pads.size(); i++) {
-                            if (adjust_pads[i] && pads_begin[i])
-                                return false;
-                        }
-                    }
-                    for (int i = 0; i < adjust_pads.size(); i++) {
-                        if (pads_end[i] < adjust_pads[i])
-                            return false;
-                    }
-                    return true;
-                }
-                else if (padMode == "SAME")
-                {
-                    for (int i = 0; i < adjust_pads.size(); i++) {
-                        if (kernel_size[i] < pads_begin[i] + 1 + adjust_pads[i])
-                            return false;
-                    }
-                    return true;
-                }
-                else if (padMode == "VALID")
-                    return false;
-            }
-
-            if (group != 1)
-            {
-                return preferableTarget == DNN_TARGET_CPU;
-            }
-            if (preferableTarget == DNN_TARGET_OPENCL || preferableTarget == DNN_TARGET_OPENCL_FP16)
-                return std::accumulate(dilations.begin(), dilations.end(), 1, std::multiplies<size_t>()) == 1;
-            return true;
-        }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 #endif  // HAVE_INF_ENGINE
         {
             return backendId == DNN_BACKEND_CUDA ||
@@ -3022,64 +2913,6 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
-    {
-        CV_Assert(!blobs.empty());
-        InferenceEngine::Layout layout = blobs[0].dims == 5? InferenceEngine::Layout::NCDHW :
-                                                             InferenceEngine::Layout::OIHW;
-
-        auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
-        if (fusedWeights)
-        {
-            ieWeights = InferenceEngine::make_shared_blob<float>({
-                            InferenceEngine::Precision::FP32,
-                            ieWeights->getTensorDesc().getDims(), layout
-                        });
-            ieWeights->allocate();
-
-            int inpCn = blobs[0].size[0];
-            Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, inpCn);
-            transpose(weightsMat, newWeights);
-        }
-
-        const int outGroupCn = blobs[0].size[1];  // Weights are in IOHW or OIDHW layout
-        const int group = numOutput / outGroupCn;
-
-        InferenceEngine::Builder::DeconvolutionLayer ieLayer(name);
-
-        ieLayer.setKernel(kernel_size);
-        ieLayer.setStrides(strides);
-        ieLayer.setDilation(dilations);
-        ieLayer.setPaddingsBegin(pads_begin);
-
-        if (padMode.empty())
-        {
-            std::vector<size_t> paddings_end;
-            for (int i = 0; i < pads_end.size(); i++) {
-                paddings_end.push_back(pads_end[i] - adjust_pads[i]);
-            }
-            ieLayer.setPaddingsEnd(paddings_end);
-        }
-        else if (padMode == "SAME")
-        {
-            std::vector<size_t> paddings_end;
-            for (int i = 0; i < pads_begin.size(); i++) {
-                paddings_end.push_back(kernel_size[i] - pads_begin[i] - 1 - adjust_pads[i]);
-            }
-            ieLayer.setPaddingsEnd(paddings_end);
-        }
-        ieLayer.setGroup((size_t)group);
-        ieLayer.setOutDepth((size_t)numOutput);
-
-        InferenceEngine::Builder::Layer l = ieLayer;
-        addConstantData("weights", ieWeights, l);
-        if (hasBias())
-            addConstantData("biases", wrapToInfEngineBlob(biasesMat, {(size_t)numOutput}, InferenceEngine::Layout::C), l);
-        return Ptr<BackendNode>(new InfEngineBackendNode(l));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
diff --git a/modules/dnn/src/layers/detection_output_layer.cpp b/modules/dnn/src/layers/detection_output_layer.cpp
index 77d86d5652..61d4f44432 100644
--- a/modules/dnn/src/layers/detection_output_layer.cpp
+++ b/modules/dnn/src/layers/detection_output_layer.cpp
@@ -221,7 +221,7 @@ public:
     {
         return backendId == DNN_BACKEND_OPENCV ||
                (backendId == DNN_BACKEND_CUDA && !_groupByClasses) ||
-               ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && !_locPredTransposed && _bboxesNormalized);
+               (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && !_locPredTransposed && _bboxesNormalized);
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -1001,30 +1001,6 @@ public:
     }
 #endif
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::DetectionOutputLayer ieLayer(name);
-
-        ieLayer.setNumClasses(_numClasses);
-        ieLayer.setShareLocation(_shareLocation);
-        ieLayer.setBackgroudLabelId(_backgroundLabelId);
-        ieLayer.setNMSThreshold(_nmsThreshold);
-        ieLayer.setTopK(_topK > 0 ? _topK : _keepTopK);
-        ieLayer.setKeepTopK(_keepTopK);
-        ieLayer.setConfidenceThreshold(_confidenceThreshold);
-        ieLayer.setVariantEncodedInTarget(_varianceEncodedInTarget);
-        ieLayer.setCodeType("caffe.PriorBoxParameter." + _codeType);
-        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(3));
-
-        InferenceEngine::Builder::Layer l = ieLayer;
-        l.getParameters()["eta"] = std::string("1.0");
-        l.getParameters()["clip"] = _clip;
-
-        return Ptr<BackendNode>(new InfEngineBackendNode(l));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp
index bfabef9d68..0085ba7449 100644
--- a/modules/dnn/src/layers/elementwise_layers.cpp
+++ b/modules/dnn/src/layers/elementwise_layers.cpp
@@ -186,14 +186,6 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::Layer ieLayer = func.initInfEngineBuilderAPI();
-        ieLayer.setName(this->name);
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
@@ -341,10 +333,6 @@ struct ReLUFunctor : public BaseFunctor
 
     bool supportBackend(int backendId, int)
     {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-            return slope >= 0 || !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
-#endif
 #ifdef HAVE_DNN_NGRAPH
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             return true;
@@ -462,13 +450,6 @@ struct ReLUFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
-    {
-        return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(slope);
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
     {
@@ -534,11 +515,14 @@ struct ReLU6Functor : public BaseFunctor
 
     bool supportBackend(int backendId, int)
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_WEBNN ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
+               backendId == DNN_BACKEND_WEBNN;
     }
 
     void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@@ -620,12 +604,6 @@ struct ReLU6Functor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
-    {
-        return InferenceEngine::Builder::ClampLayer("").setMinValue(minValue).setMaxValue(maxValue);
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -743,12 +721,6 @@ struct BaseDefaultFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
-    {
-        CV_Error(Error::StsNotImplemented, "");
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -782,10 +754,13 @@ struct TanHFunctor : public BaseDefaultFunctor<TanHFunctor>
 
     bool supportBackend(int backendId, int)
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
-               backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
+               backendId == DNN_BACKEND_HALIDE;
     }
 
     inline float calculate(float x) const
@@ -808,13 +783,6 @@ struct TanHFunctor : public BaseDefaultFunctor<TanHFunctor>
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
-    {
-        return InferenceEngine::Builder::TanHLayer("");
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
     {
@@ -937,10 +905,13 @@ struct SigmoidFunctor : public BaseDefaultFunctor<SigmoidFunctor>
 
     bool supportBackend(int backendId, int)
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
-               backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||  backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
+               backendId == DNN_BACKEND_HALIDE;
     }
 
     inline float calculate(float x) const
@@ -963,12 +934,6 @@ struct SigmoidFunctor : public BaseDefaultFunctor<SigmoidFunctor>
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
-    {
-        return InferenceEngine::Builder::SigmoidLayer("");
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -992,10 +957,13 @@ struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
 
     bool supportBackend(int backendId, int)
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
-               backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||  backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
+               backendId == DNN_BACKEND_HALIDE;
     }
 
     inline float calculate(float x) const
@@ -1023,13 +991,6 @@ struct ELUFunctor : public BaseDefaultFunctor<ELUFunctor>
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
-    {
-        return InferenceEngine::Builder::ELULayer("");
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
     {
@@ -1050,8 +1011,8 @@ struct AbsValFunctor : public BaseDefaultFunctor<AbsValFunctor>
     bool supportBackend(int backendId, int)
     {
 #ifdef HAVE_INF_ENGINE
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-            return !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
 #endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
@@ -1078,12 +1039,6 @@ struct AbsValFunctor : public BaseDefaultFunctor<AbsValFunctor>
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
-    {
-        return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(-0.999999f);
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -1930,14 +1885,15 @@ struct PowerFunctor : public BaseFunctor
 
     bool supportBackend(int backendId, int targetId)
     {
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-            return (targetId != DNN_TARGET_OPENCL && targetId != DNN_TARGET_OPENCL_FP16) || power == 1.0 || power == 0.5;
+#ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             return true;
-        else
+#endif
+        {
             return backendId == DNN_BACKEND_OPENCV ||
                    backendId == DNN_BACKEND_CUDA ||
                    backendId == DNN_BACKEND_HALIDE;
+        }
     }
 
     void finalize()
@@ -2029,14 +1985,6 @@ struct PowerFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
-    {
-        return InferenceEngine::Builder::PowerLayer("").setPower(power)
-                                                       .setScale(scale)
-                                                       .setShift(shift);
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -2189,10 +2137,13 @@ struct ChannelsPReLUFunctor : public BaseFunctor
 
     bool supportBackend(int backendId, int)
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
-               backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
+               backendId == DNN_BACKEND_HALIDE;
     }
 
     void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@@ -2282,15 +2233,6 @@ struct ChannelsPReLUFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
-    {
-        InferenceEngine::Builder::Layer l = InferenceEngine::Builder::PReLULayer("");
-        const size_t numChannels = scale.total();
-        addConstantData("weights", wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C), l);
-        return l;
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
diff --git a/modules/dnn/src/layers/eltwise_layer.cpp b/modules/dnn/src/layers/eltwise_layer.cpp
index 2c473ff412..43d925055c 100644
--- a/modules/dnn/src/layers/eltwise_layer.cpp
+++ b/modules/dnn/src/layers/eltwise_layer.cpp
@@ -164,6 +164,11 @@ public:
         if (hasVecInput && ELTWISE_CHANNNELS_SAME)
             return backendId == DNN_BACKEND_OPENCV;
 
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return channelsMode == ELTWISE_CHANNNELS_SAME;
+#endif
+
         if (backendId == DNN_BACKEND_CUDA)
         {
             if(channelsModeInput == ELTWISE_CHANNNELS_INPUT_0 || channelsModeInput == ELTWISE_CHANNNELS_INPUT_0_TRUNCATE)
@@ -172,9 +177,8 @@ public:
         }
 
         return backendId == DNN_BACKEND_OPENCV ||
-               (backendId == DNN_BACKEND_HALIDE && op != DIV) ||  // TODO: not implemented, see PR #15811
-               ((((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (preferableTarget != DNN_TARGET_OPENCL || coeffs.empty()))
-                || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && channelsMode == ELTWISE_CHANNNELS_SAME));
+               (backendId == DNN_BACKEND_HALIDE && op != DIV)  // TODO: not implemented, see PR #15811
+               ;
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -837,34 +841,6 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::EltwiseLayer ieLayer(name);
-
-        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
-
-        if (op == SUM)
-            ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::SUM);
-        else if (op == PROD)
-            ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MUL);
-        else if (op == DIV)
-            ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::DIV);
-        else if (op == MAX)
-            ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MAX);
-        else if (op == MIN)
-            ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MIN);
-        else
-            CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
-
-        InferenceEngine::Builder::Layer l = ieLayer;
-        if (!coeffs.empty())
-            l.getParameters()["coeff"] = coeffs;
-
-        return Ptr<BackendNode>(new InfEngineBackendNode(l));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
diff --git a/modules/dnn/src/layers/flatten_layer.cpp b/modules/dnn/src/layers/flatten_layer.cpp
index 69bc422ee3..b3f57dc7cd 100644
--- a/modules/dnn/src/layers/flatten_layer.cpp
+++ b/modules/dnn/src/layers/flatten_layer.cpp
@@ -72,9 +72,12 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_CUDA ||
-               ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
+               backendId == DNN_BACKEND_CUDA;
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -171,25 +174,10 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::Layer ieLayer(name);
-        ieLayer.setName(name);
-        ieLayer.setType("Flatten");
-        ieLayer.getParameters()["axis"] = (size_t)_startAxis;
-        ieLayer.getParameters()["end_axis"] = _endAxis;  // Do not cast to size_t because it might be negative.
-        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
-        ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
-
 #ifdef HAVE_DNN_NGRAPH
-virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
-                                    const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
-{
+    virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
+                                        const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
+    {
         auto& ieInpNode = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
         std::vector<size_t> dims = ieInpNode->get_shape();
 
diff --git a/modules/dnn/src/layers/fully_connected_layer.cpp b/modules/dnn/src/layers/fully_connected_layer.cpp
index 99acba908b..5d0ad5fde7 100644
--- a/modules/dnn/src/layers/fully_connected_layer.cpp
+++ b/modules/dnn/src/layers/fully_connected_layer.cpp
@@ -148,12 +148,15 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return axis == 1;
+#endif
+
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                (backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1) ||
-               (backendId == DNN_BACKEND_WEBNN && axis == 1) ||
-               (((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && !blobs.empty()) ||
-                backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && axis == 1);
+               (backendId == DNN_BACKEND_WEBNN && axis == 1);
     }
 
     virtual bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
@@ -570,23 +573,6 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::FullyConnectedLayer ieLayer(name);
-
-        const int outNum = blobs[0].size[0];
-        ieLayer.setOutputNum(outNum);
-
-        InferenceEngine::Builder::Layer l = ieLayer;
-        addConstantData("weights", wrapToInfEngineBlob(blobs[0], {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1], 1, 1}, InferenceEngine::Layout::OIHW), l);
-        if (bias)
-            addConstantData("biases", wrapToInfEngineBlob(blobs[1], {(size_t)outNum}, InferenceEngine::Layout::C), l);
-
-        return Ptr<BackendNode>(new InfEngineBackendNode(l));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
diff --git a/modules/dnn/src/layers/lrn_layer.cpp b/modules/dnn/src/layers/lrn_layer.cpp
index 224441b0e7..6c3a654159 100644
--- a/modules/dnn/src/layers/lrn_layer.cpp
+++ b/modules/dnn/src/layers/lrn_layer.cpp
@@ -99,12 +99,10 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             return bias == (int)bias;
-        }
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
-            return bias == (int)bias;
-        }
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                backendId == DNN_BACKEND_HALIDE ||
@@ -444,24 +442,6 @@ public:
 #endif  // HAVE_HALIDE
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        float alphaSize = alpha;
-        if (!normBySize)
-            alphaSize *= (type == SPATIAL_NRM ? size*size : size);
-
-        InferenceEngine::Builder::NormLayer ieLayer(name);
-        ieLayer.setSize(size);
-        ieLayer.setAlpha(alphaSize);
-        ieLayer.setBeta(beta);
-        ieLayer.setAcrossMaps(type == CHANNEL_NRM);
-
-        InferenceEngine::Builder::Layer l = ieLayer;
-        l.getParameters()["k"] = bias;
-        return Ptr<BackendNode>(new InfEngineBackendNode(l));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/mvn_layer.cpp b/modules/dnn/src/layers/mvn_layer.cpp
index 455f4997df..dc23656b7a 100644
--- a/modules/dnn/src/layers/mvn_layer.cpp
+++ b/modules/dnn/src/layers/mvn_layer.cpp
@@ -124,14 +124,7 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        {
-            bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
-            return !zeroDev && (!isMyriad || eps <= 1e-7f);
-        }
-#endif
-#ifdef HAVE_DNN_NGRAPH
+#ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             return true;
 #endif
@@ -387,16 +380,6 @@ public:
         }
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::MVNLayer ieLayer(name);
-        ieLayer.setAcrossChannels(acrossChannels);
-        ieLayer.setNormalize(normVariance);
-        ieLayer.setEpsilon(eps);
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
diff --git a/modules/dnn/src/layers/normalize_bbox_layer.cpp b/modules/dnn/src/layers/normalize_bbox_layer.cpp
index 236f2e43f1..2017d76801 100644
--- a/modules/dnn/src/layers/normalize_bbox_layer.cpp
+++ b/modules/dnn/src/layers/normalize_bbox_layer.cpp
@@ -70,17 +70,15 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
             if (pnorm != 2)
                 return false;
 
-            bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
-            if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && isMyriad)
-                return !acrossSpatial;
-
             return startAxis == 1;
         }
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                (backendId == DNN_BACKEND_CUDA && (pnorm == 1 || pnorm == 2));
     }
@@ -270,58 +268,6 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
-    {
-        InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
-        std::vector<size_t> dims = input->getDims();
-        if (dims.size() == 4)
-        {
-            InferenceEngine::Builder::NormalizeLayer ieLayer(name);
-
-            ieLayer.setChannelShared(false);
-            ieLayer.setAcrossMaps(acrossSpatial);
-            ieLayer.setEpsilon(epsilon);
-
-            InferenceEngine::Builder::Layer l = ieLayer;
-            const int numChannels = dims[1];
-            InferenceEngine::Blob::Ptr weights;
-            if (blobs.empty())
-            {
-                weights = InferenceEngine::make_shared_blob<float>({
-                              InferenceEngine::Precision::FP32,
-                              {(size_t)numChannels}, InferenceEngine::Layout::C
-                          });
-                weights->allocate();
-
-                Mat weightsMat = infEngineBlobToMat(weights).reshape(1, numChannels);
-                Mat(numChannels, 1, CV_32F, Scalar(1)).copyTo(weightsMat);
-                l.getParameters()["channel_shared"] = false;
-            }
-            else
-            {
-                CV_Assert(numChannels == blobs[0].total());
-                weights = wrapToInfEngineBlob(blobs[0], {(size_t)numChannels}, InferenceEngine::Layout::C);
-                l.getParameters()["channel_shared"] = blobs[0].total() == 1;
-            }
-            addConstantData("weights", weights, l);
-            l.getParameters()["across_spatial"] = acrossSpatial;
-            return Ptr<BackendNode>(new InfEngineBackendNode(l));
-        }
-        else
-        {
-            InferenceEngine::Builder::GRNLayer ieLayer(name);
-            ieLayer.setBeta(epsilon);
-
-            InferenceEngine::Builder::Layer l = ieLayer;
-            l.getParameters()["bias"] = epsilon;
-
-            return Ptr<BackendNode>(new InfEngineBackendNode(l));
-        }
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
-
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/not_implemented_layer.cpp b/modules/dnn/src/layers/not_implemented_layer.cpp
index c4b1343902..3fd52c09d9 100644
--- a/modules/dnn/src/layers/not_implemented_layer.cpp
+++ b/modules/dnn/src/layers/not_implemented_layer.cpp
@@ -87,11 +87,6 @@ public:
         CV_Error(Error::StsNotImplemented, msg);
     }
 
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
-    {
-        CV_Error(Error::StsNotImplemented, msg);
-    }
-
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
     {
diff --git a/modules/dnn/src/layers/padding_layer.cpp b/modules/dnn/src/layers/padding_layer.cpp
index 7534145f53..aea8ab3168 100644
--- a/modules/dnn/src/layers/padding_layer.cpp
+++ b/modules/dnn/src/layers/padding_layer.cpp
@@ -102,10 +102,10 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
 #ifdef HAVE_INF_ENGINE
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
             bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
-            if (INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) && isMyriad)
+            if (isMyriad)
                 return dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0;
 
             return (dstRanges.size() <= 4 || !isArmComputePlugin());
@@ -219,30 +219,6 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::Layer ieLayer(name);
-        ieLayer.setName(name);
-        ieLayer.setType("Pad");
-
-        std::vector<int> begins(paddings.size(), 0), ends(paddings.size(), 0);
-        for (int i = 0; i < paddings.size(); ++i)
-        {
-            begins[i] = paddings[i].first;
-            ends[i] = paddings[i].second;
-        }
-        ieLayer.getParameters()["pads_begin"] = begins;
-        ieLayer.getParameters()["pads_end"] = ends;
-        ieLayer.getParameters()["pad_mode"] = paddingType;
-        if (paddingType == "constant")
-            ieLayer.getParameters()["pad_value"] = paddingValue;
-
-        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
-        ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
diff --git a/modules/dnn/src/layers/permute_layer.cpp b/modules/dnn/src/layers/permute_layer.cpp
index 9e66eb6a64..033b3d9aee 100644
--- a/modules/dnn/src/layers/permute_layer.cpp
+++ b/modules/dnn/src/layers/permute_layer.cpp
@@ -115,13 +115,16 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
 #ifdef HAVE_INF_ENGINE
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && preferableTarget == DNN_TARGET_CPU)
-            return _order.size() <= 4 || !isArmComputePlugin();
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        {
+            if (preferableTarget == DNN_TARGET_CPU)
+                return _order.size() <= 4 || !isArmComputePlugin();
+            return true;
+        }
 #endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                backendId == DNN_BACKEND_WEBNN ||
-               ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine()) ||
                (backendId == DNN_BACKEND_VKCOM && haveVulkan());
     }
 
@@ -418,16 +421,6 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::PermuteLayer ieLayer(name);
-        ieLayer.setOrder(_order);
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
-
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/pooling_layer.cpp b/modules/dnn/src/layers/pooling_layer.cpp
index 7cb86a9515..f8616a4184 100644
--- a/modules/dnn/src/layers/pooling_layer.cpp
+++ b/modules/dnn/src/layers/pooling_layer.cpp
@@ -199,34 +199,13 @@ public:
         {
             return type == MAX || type == AVE || type == ROI;
         }
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        {
-            if (computeMaxIdx)
-                return false;
-            if (kernel_size.size() == 3)
-                return preferableTarget == DNN_TARGET_CPU;
-            if (kernel_size.size() == 1)
-                return false;
-            if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL) {
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-                if (type == MAX && (pads_begin[1] == 1 && pads_begin[0] == 1) && (strides[0] == 2 && strides[1] == 2)) {
-                    return !isMyriadX();
-                }
-#endif
-                return type == MAX || type == AVE;
-            }
-            else
-                return type != STOCHASTIC && type != SUM;
-        }
-#endif
+#ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
-#ifdef HAVE_DNN_NGRAPH
             return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin());
-#endif
         }
-        else if (backendId == DNN_BACKEND_OPENCV)
+#endif
+        if (backendId == DNN_BACKEND_OPENCV)
         {
             if (kernel_size.size() == 3)
                 return preferableTarget == DNN_TARGET_CPU;
@@ -550,54 +529,6 @@ public:
             return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        if (type == MAX || type == AVE)
-        {
-            InferenceEngine::Builder::PoolingLayer ieLayer(name);
-
-            ieLayer.setKernel(kernel_size);
-            ieLayer.setStrides(strides);
-            ieLayer.setPaddingsBegin(pads_begin);
-            ieLayer.setPaddingsEnd(pads_end);
-
-            ieLayer.setPoolingType(type == MAX ?
-                                   InferenceEngine::Builder::PoolingLayer::PoolingType::MAX :
-                                   InferenceEngine::Builder::PoolingLayer::PoolingType::AVG);
-            ieLayer.setRoundingType(ceilMode ?
-                                    InferenceEngine::Builder::PoolingLayer::RoundingType::CEIL :
-                                    InferenceEngine::Builder::PoolingLayer::RoundingType::FLOOR);
-            ieLayer.setExcludePad(!avePoolPaddedArea);
-
-            InferenceEngine::Builder::Layer l = ieLayer;
-            if (!padMode.empty())
-                l.getParameters()["auto_pad"] = padMode == "VALID" ? std::string("valid") : std::string("same_upper");
-            return Ptr<BackendNode>(new InfEngineBackendNode(l));
-        }
-        else if (type == ROI)
-        {
-            InferenceEngine::Builder::ROIPoolingLayer ieLayer(name);
-            ieLayer.setSpatialScale(spatialScale);
-            ieLayer.setPooled({pooledSize.height, pooledSize.width});
-            ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(2));
-            return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-        }
-        else if (type == PSROI)
-        {
-            InferenceEngine::Builder::PSROIPoolingLayer ieLayer(name);
-            ieLayer.setSpatialScale(spatialScale);
-            ieLayer.setOutputDim(psRoiOutChannels);
-            ieLayer.setGroupSize(pooledSize.width);
-            ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(2));
-            return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-        }
-        else
-            CV_Error(Error::StsNotImplemented, "Unsupported pooling type");
-        return Ptr<BackendNode>();
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
diff --git a/modules/dnn/src/layers/prior_box_layer.cpp b/modules/dnn/src/layers/prior_box_layer.cpp
index f7340b1e67..160b36c18d 100644
--- a/modules/dnn/src/layers/prior_box_layer.cpp
+++ b/modules/dnn/src/layers/prior_box_layer.cpp
@@ -298,9 +298,7 @@ public:
 #endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() &&
-                   ( _explicitSizes || (_minSize.size() == 1 && _maxSize.size() <= 1)))
-               || (backendId == DNN_BACKEND_VKCOM && haveVulkan());
+               (backendId == DNN_BACKEND_VKCOM && haveVulkan());
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -510,69 +508,6 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        if (_explicitSizes)
-        {
-            InferenceEngine::Builder::PriorBoxClusteredLayer ieLayer(name);
-            ieLayer.setSteps({_stepY, _stepX});
-
-            CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
-            ieLayer.setOffset(_offsetsX[0]);
-
-            ieLayer.setClip(_clip);
-            ieLayer.setFlip(false);  // We already flipped aspect ratios.
-
-            InferenceEngine::Builder::Layer l = ieLayer;
-
-            CV_Assert_N(!_boxWidths.empty(), !_boxHeights.empty(), !_variance.empty());
-            CV_Assert(_boxWidths.size() == _boxHeights.size());
-            l.getParameters()["width"] = _boxWidths;
-            l.getParameters()["height"] = _boxHeights;
-            l.getParameters()["variance"] = _variance;
-            return Ptr<BackendNode>(new InfEngineBackendNode(l));
-        }
-        else
-        {
-            InferenceEngine::Builder::PriorBoxLayer ieLayer(name);
-
-            CV_Assert(!_explicitSizes);
-            ieLayer.setMinSize(_minSize[0]);
-            if (!_maxSize.empty())
-                ieLayer.setMaxSize(_maxSize[0]);
-
-            CV_CheckEQ(_offsetsX.size(), (size_t)1, ""); CV_CheckEQ(_offsetsY.size(), (size_t)1, ""); CV_CheckEQ(_offsetsX[0], _offsetsY[0], "");
-            ieLayer.setOffset(_offsetsX[0]);
-
-            ieLayer.setClip(_clip);
-            ieLayer.setFlip(false);  // We already flipped aspect ratios.
-
-            InferenceEngine::Builder::Layer l = ieLayer;
-            if (_stepX == _stepY)
-            {
-                l.getParameters()["step"] = _stepX;
-                l.getParameters()["step_h"] = 0.0f;
-                l.getParameters()["step_w"] = 0.0f;
-            }
-            else
-            {
-                l.getParameters()["step"] = 0.0f;
-                l.getParameters()["step_h"] = _stepY;
-                l.getParameters()["step_w"] = _stepX;
-            }
-            if (!_aspectRatios.empty())
-            {
-                l.getParameters()["aspect_ratio"] = _aspectRatios;
-            }
-            CV_Assert(!_variance.empty());
-            l.getParameters()["variance"] = _variance;
-            return Ptr<BackendNode>(new InfEngineBackendNode(l));
-        }
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
-
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
     {
diff --git a/modules/dnn/src/layers/proposal_layer.cpp b/modules/dnn/src/layers/proposal_layer.cpp
index aeb5d44a47..e9edcf1547 100644
--- a/modules/dnn/src/layers/proposal_layer.cpp
+++ b/modules/dnn/src/layers/proposal_layer.cpp
@@ -96,7 +96,7 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
 #ifdef HAVE_INF_ENGINE
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
             bool isMyriad = preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL;
             return !isMyriad;
@@ -338,32 +338,6 @@ public:
         layerOutputs[0].col(2).copyTo(dst);
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::ProposalLayer ieLayer(name);
-
-        ieLayer.setBaseSize(baseSize);
-        ieLayer.setFeatStride(featStride);
-        ieLayer.setMinSize(16);
-        ieLayer.setNMSThresh(nmsThreshold);
-        ieLayer.setPostNMSTopN(keepTopAfterNMS);
-        ieLayer.setPreNMSTopN(keepTopBeforeNMS);
-
-        std::vector<float> scalesVec(scales.size());
-        for (int i = 0; i < scales.size(); ++i)
-            scalesVec[i] = scales.get<float>(i);
-        ieLayer.setScale(scalesVec);
-
-        std::vector<float> ratiosVec(ratios.size());
-        for (int i = 0; i < ratios.size(); ++i)
-            ratiosVec[i] = ratios.get<float>(i);
-        ieLayer.setRatio(ratiosVec);
-
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
diff --git a/modules/dnn/src/layers/reorg_layer.cpp b/modules/dnn/src/layers/reorg_layer.cpp
index 797df4819d..ac7d1abfb1 100644
--- a/modules/dnn/src/layers/reorg_layer.cpp
+++ b/modules/dnn/src/layers/reorg_layer.cpp
@@ -151,10 +151,12 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_CUDA ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
+               backendId == DNN_BACKEND_CUDA;
     }
 
 #ifdef HAVE_OPENCL
@@ -198,16 +200,6 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::ReorgYoloLayer ieLayer(name);
-        ieLayer.setStride(reorgStride);
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
-
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/reshape_layer.cpp b/modules/dnn/src/layers/reshape_layer.cpp
index 0ba3abf047..f62235dc20 100644
--- a/modules/dnn/src/layers/reshape_layer.cpp
+++ b/modules/dnn/src/layers/reshape_layer.cpp
@@ -202,10 +202,13 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
-               backendId == DNN_BACKEND_WEBNN ||
-               ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && haveInfEngine());
+               backendId == DNN_BACKEND_WEBNN;
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -306,17 +309,6 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::ReshapeLayer ieLayer(name);
-        CV_Assert(outShapes.size() == 1);
-        ieLayer.setDims(outShapes[0]);
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
-
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/resize_layer.cpp b/modules/dnn/src/layers/resize_layer.cpp
index 42eb2e2331..ab640dbf3f 100644
--- a/modules/dnn/src/layers/resize_layer.cpp
+++ b/modules/dnn/src/layers/resize_layer.cpp
@@ -78,7 +78,7 @@ public:
             return interpolation == "nearest" || interpolation == "bilinear" || interpolation == "opencv_linear";
 
 #ifdef HAVE_INF_ENGINE
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
             return (interpolation == "nearest" && scaleWidth == scaleHeight) ||
                    (interpolation == "bilinear");
@@ -308,38 +308,6 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::Layer ieLayer(name);
-        ieLayer.setName(name);
-        if (interpolation == "nearest")
-        {
-            ieLayer.setType("Resample");
-            ieLayer.getParameters()["type"] = std::string("caffe.ResampleParameter.NEAREST");
-            ieLayer.getParameters()["antialias"] = false;
-            if (scaleWidth != scaleHeight)
-                CV_Error(Error::StsNotImplemented, "resample with sw != sh");
-            ieLayer.getParameters()["factor"] = 1.0f / scaleWidth;
-        }
-        else if (interpolation == "bilinear")
-        {
-            ieLayer.setType("Interp");
-            ieLayer.getParameters()["pad_beg"] = 0;
-            ieLayer.getParameters()["pad_end"] = 0;
-            ieLayer.getParameters()["align_corners"] = alignCorners;
-        }
-        else
-            CV_Error(Error::StsNotImplemented, "Unsupported interpolation: " + interpolation);
-        ieLayer.getParameters()["width"] = outWidth;
-        ieLayer.getParameters()["height"] = outHeight;
-        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
-        ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
-
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/scale_layer.cpp b/modules/dnn/src/layers/scale_layer.cpp
index fcee451556..d727734cf3 100644
--- a/modules/dnn/src/layers/scale_layer.cpp
+++ b/modules/dnn/src/layers/scale_layer.cpp
@@ -78,11 +78,13 @@ public:
         {
             return backendId == DNN_BACKEND_OPENCV;
         }
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return axis > 0;
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                backendId == DNN_BACKEND_HALIDE ||
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && axis == 1 && !blobs.empty()) ||
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && axis > 0) ||
                (backendId == DNN_BACKEND_WEBNN && axis >0);
     }
 
@@ -314,34 +316,6 @@ public:
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
-    {
-        InferenceEngine::Builder::Layer l = InferenceEngine::Builder::ScaleShiftLayer(name);
-
-        CV_Assert(!blobs.empty());
-        const size_t numChannels = blobs[0].total();
-        if (hasWeights)
-        {
-            addConstantData("weights", wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C), l);
-        }
-        else
-        {
-            auto weights = InferenceEngine::make_shared_blob<float>({
-                               InferenceEngine::Precision::FP32, {(size_t)numChannels},
-                               InferenceEngine::Layout::C
-                           });
-            weights->allocate();
-            float* buf = weights->buffer().as<float*>();
-            std::fill(buf, buf + numChannels, 1);
-            addConstantData("weights", weights, l);
-        }
-        if (hasBias)
-            addConstantData("biases", wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C), l);
-        return Ptr<BackendNode>(new InfEngineBackendNode(l));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
-
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/slice_layer.cpp b/modules/dnn/src/layers/slice_layer.cpp
index 20b493636a..4370e566a5 100644
--- a/modules/dnn/src/layers/slice_layer.cpp
+++ b/modules/dnn/src/layers/slice_layer.cpp
@@ -166,12 +166,7 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-            return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
-                sliceRanges.size() == 1 && sliceRanges[0].size() == 4 && !hasSteps;
-#endif
-#ifdef HAVE_DNN_NGRAPH
+#ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             return sliceRanges.size() == 1 && !hasSteps;
 #endif
@@ -573,64 +568,6 @@ public:
     }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
-    {
-        CV_Assert_N(finalSliceRanges.size() == 1, inputs.size() <= 2);
-
-        std::vector<size_t> axes, offsets, dims;
-        int from, to, step;
-        int numDims = finalSliceRanges[0].size();
-        if (preferableTarget == DNN_TARGET_MYRIAD || preferableTarget == DNN_TARGET_HDDL)
-        {
-            from = axis;
-            to = numDims;
-            step = 1;
-        }
-        else
-        {
-            from = numDims - 1;
-            to = axis - 1;
-            step = -1;
-        }
-        for (int i = from; i != to; i += step)
-        {
-            axes.push_back(i);
-            offsets.push_back(finalSliceRanges[0][i].start);
-            dims.push_back(finalSliceRanges[0][i].size());
-        }
-
-        InferenceEngine::Builder::Layer ieLayer(name);
-        ieLayer.setName(name);
-        ieLayer.setType("Crop");
-        ieLayer.getParameters()["axis"] = axes;
-        ieLayer.getParameters()["dim"] = dims;
-        ieLayer.getParameters()["offset"] = offsets;
-        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(2));
-        ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
-
-        if (inputs.size() != 2)
-        {
-            std::vector<size_t> outShape(numDims);
-            for (int i = 0; i < numDims; ++i)
-                outShape[i] = finalSliceRanges[0][i].size();
-
-            ieLayer.getInputPorts()[1].setParameter("type", "weights");
-
-            auto shapeSource = InferenceEngine::make_shared_blob<float>({
-                                   InferenceEngine::Precision::FP32, outShape,
-                                   InferenceEngine::Layout::ANY
-                               });
-            shapeSource->allocate();
-            addConstantData("weights", shapeSource, ieLayer);
-        }
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif
-#endif
-
-
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
                                         const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
diff --git a/modules/dnn/src/layers/softmax_layer.cpp b/modules/dnn/src/layers/softmax_layer.cpp
index db2951808f..790f181325 100644
--- a/modules/dnn/src/layers/softmax_layer.cpp
+++ b/modules/dnn/src/layers/softmax_layer.cpp
@@ -99,6 +99,10 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return true;
+#endif
 #ifdef HAVE_WEBNN
         if (backendId == DNN_BACKEND_WEBNN) {
             // TODO: support logSoftMax
@@ -112,8 +116,6 @@ public:
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                (backendId == DNN_BACKEND_HALIDE && haveHalide() && axisRaw == 1) ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && haveInfEngine() && !logSoftMax) ||
                (backendId == DNN_BACKEND_VKCOM && haveVulkan());
     }
 
@@ -360,17 +362,6 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
-    {
-        InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
-
-        InferenceEngine::Builder::SoftMaxLayer ieLayer(name);
-        ieLayer.setAxis(normalize_axis(axisRaw, input->getDims().size()));
-
-        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-    }
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
diff --git a/modules/dnn/src/op_inf_engine.cpp b/modules/dnn/src/op_inf_engine.cpp
index d9b98404c3..2899545c6d 100644
--- a/modules/dnn/src/op_inf_engine.cpp
+++ b/modules/dnn/src/op_inf_engine.cpp
@@ -20,52 +20,17 @@ namespace cv { namespace dnn {
 
 #ifdef HAVE_INF_ENGINE
 
-static Backend parseInferenceEngineBackendType(const cv::String& backend)
-{
-    CV_Assert(!backend.empty());
-    if (backend == CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        return DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
-    if (backend == CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API)
-        return DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019;
-    CV_Error(Error::StsBadArg, cv::format("Unknown IE backend: %s", backend.c_str()));
-}
-static const char* dumpInferenceEngineBackendType(Backend backend)
-{
-    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        return CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH;
-    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        return CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API;
-    CV_Error(Error::StsBadArg, cv::format("Invalid backend ID for IE: %d", backend));
-}
-Backend& getInferenceEngineBackendTypeParam()
-{
-    static Backend param = parseInferenceEngineBackendType(
-        utils::getConfigurationParameterString("OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE",
-#ifdef HAVE_DNN_NGRAPH
-            CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
-#elif defined(HAVE_DNN_IE_NN_BUILDER_2019)
-            CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API
-#else
-#error "Build configuration error: nGraph or NN Builder API backend should be enabled"
-#endif
-        )
-    );
-    return param;
-}
-
 CV__DNN_INLINE_NS_BEGIN
 
 cv::String getInferenceEngineBackendType()
 {
-    return dumpInferenceEngineBackendType(getInferenceEngineBackendTypeParam());
+    return "NGRAPH";
 }
 cv::String setInferenceEngineBackendType(const cv::String& newBackendType)
 {
-    Backend newBackend = parseInferenceEngineBackendType(newBackendType);
-    Backend& param = getInferenceEngineBackendTypeParam();
-    Backend old = param;
-    param = newBackend;
-    return dumpInferenceEngineBackendType(old);
+    if (newBackendType != "NGRAPH")
+        CV_Error(Error::StsNotImplemented, cv::format("DNN/IE: only NGRAPH backend is supported: %s", newBackendType.c_str()));
+    return newBackendType;
 }
 
 CV__DNN_INLINE_NS_END
@@ -98,508 +63,6 @@ void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
 }
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-
-// For networks with input layer which has an empty name, IE generates a name id[some_number].
-// OpenCV lets users use an empty input name and to prevent unexpected naming,
-// we can use some predefined name.
-static std::string kDefaultInpLayerName = "empty_inp_layer_name";
-static std::string kOpenCVLayersType = "OpenCVLayer";
-
-static std::string shapesToStr(const std::vector<Mat>& mats)
-{
-    std::ostringstream shapes;
-    shapes << mats.size() << " ";
-    for (const Mat& m : mats)
-    {
-        shapes << m.dims << " ";
-        for (int i = 0; i < m.dims; ++i)
-            shapes << m.size[i] << " ";
-    }
-    return shapes.str();
-}
-
-static void strToShapes(const std::string& str, std::vector<std::vector<size_t> >& shapes)
-{
-    std::istringstream ss(str);
-    int num, dims;
-    ss >> num;
-    shapes.resize(num);
-    for (int i = 0; i < num; ++i)
-    {
-        ss >> dims;
-        shapes[i].resize(dims);
-        for (int j = 0; j < dims; ++j)
-            ss >> shapes[i][j];
-    }
-}
-
-class InfEngineCustomLayer : public InferenceEngine::ILayerExecImpl
-{
-public:
-    explicit InfEngineCustomLayer(const InferenceEngine::CNNLayer& layer) : cnnLayer(layer)
-    {
-        std::istringstream iss(layer.GetParamAsString("impl"));
-        size_t ptr;
-        iss >> ptr;
-        cvLayer = (Layer*)ptr;
-
-        std::vector<std::vector<size_t> > shapes;
-        strToShapes(layer.GetParamAsString("internals"), shapes);
-        internals.resize(shapes.size());
-        for (int i = 0; i < shapes.size(); ++i)
-            internals[i].create(std::vector<int>(shapes[i].begin(), shapes[i].end()), CV_32F);
-    }
-
-    virtual InferenceEngine::StatusCode execute(std::vector<InferenceEngine::Blob::Ptr>& inputs,
-                                                std::vector<InferenceEngine::Blob::Ptr>& outputs,
-                                                InferenceEngine::ResponseDesc *resp) noexcept
-    {
-        std::vector<Mat> inpMats, outMats;
-        infEngineBlobsToMats(inputs, inpMats);
-        infEngineBlobsToMats(outputs, outMats);
-
-        try
-        {
-            cvLayer->forward(inpMats, outMats, internals);
-            return InferenceEngine::StatusCode::OK;
-        }
-        catch (...)
-        {
-            return InferenceEngine::StatusCode::GENERAL_ERROR;
-        }
-    }
-
-    virtual InferenceEngine::StatusCode
-    getSupportedConfigurations(std::vector<InferenceEngine::LayerConfig>& conf,
-                               InferenceEngine::ResponseDesc* resp) noexcept
-    {
-        std::vector<InferenceEngine::DataConfig> inDataConfig;
-        std::vector<InferenceEngine::DataConfig> outDataConfig;
-        for (auto& it : cnnLayer.insData)
-        {
-            InferenceEngine::DataConfig conf;
-            conf.desc = it.lock()->getTensorDesc();
-            inDataConfig.push_back(conf);
-        }
-
-        for (auto& it : cnnLayer.outData)
-        {
-            InferenceEngine::DataConfig conf;
-            conf.desc = it->getTensorDesc();
-            outDataConfig.push_back(conf);
-        }
-
-        InferenceEngine::LayerConfig layerConfig;
-        layerConfig.inConfs = inDataConfig;
-        layerConfig.outConfs = outDataConfig;
-
-        conf.push_back(layerConfig);
-        return InferenceEngine::StatusCode::OK;
-    }
-
-    InferenceEngine::StatusCode init(InferenceEngine::LayerConfig& config,
-                                     InferenceEngine::ResponseDesc *resp) noexcept
-    {
-        return InferenceEngine::StatusCode::OK;
-    }
-
-private:
-    InferenceEngine::CNNLayer cnnLayer;
-    dnn::Layer* cvLayer;
-    std::vector<Mat> internals;
-};
-
-class InfEngineCustomLayerShapeInfer : public InferenceEngine::IShapeInferImpl
-{
-public:
-      InferenceEngine::StatusCode
-      inferShapes(const std::vector<InferenceEngine::Blob::CPtr>& inBlobs,
-                  const std::map<std::string, std::string>& params,
-                  const std::map<std::string, InferenceEngine::Blob::Ptr>& blobs,
-                  std::vector<InferenceEngine::SizeVector>& outShapes,
-                  InferenceEngine::ResponseDesc* desc) noexcept override
-      {
-          strToShapes(params.at("outputs"), outShapes);
-          return InferenceEngine::StatusCode::OK;
-      }
-};
-
-class InfEngineCustomLayerFactory : public InferenceEngine::ILayerImplFactory {
-public:
-    explicit InfEngineCustomLayerFactory(const InferenceEngine::CNNLayer* layer) : cnnLayer(*layer) {}
-
-    InferenceEngine::StatusCode
-    getImplementations(std::vector<InferenceEngine::ILayerImpl::Ptr>& impls,
-                       InferenceEngine::ResponseDesc* resp) noexcept override {
-        impls.push_back(std::make_shared<InfEngineCustomLayer>(cnnLayer));
-        return InferenceEngine::StatusCode::OK;
-    }
-
-private:
-    InferenceEngine::CNNLayer cnnLayer;
-};
-
-InferenceEngine::StatusCode InfEngineExtension::getFactoryFor(
-        InferenceEngine::ILayerImplFactory*& factory,
-        const InferenceEngine::CNNLayer* cnnLayer,
-        InferenceEngine::ResponseDesc* resp
-) noexcept
-{
-    if (cnnLayer->type != kOpenCVLayersType)
-        return InferenceEngine::StatusCode::NOT_IMPLEMENTED;
-    factory = new InfEngineCustomLayerFactory(cnnLayer);
-    return InferenceEngine::StatusCode::OK;
-}
-
-InfEngineBackendNode::InfEngineBackendNode(const InferenceEngine::Builder::Layer& _layer)
-    : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019), layer(_layer) {}
-
-    InfEngineBackendNode::InfEngineBackendNode(Ptr<Layer>& cvLayer_, std::vector<Mat*>& inputs,
-                                               std::vector<Mat>& outputs,
-                                               std::vector<Mat>& internals)
-        : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019), layer(cvLayer_->name),
-          cvLayer(cvLayer_)
-{
-    CV_Assert(!cvLayer->name.empty());
-    layer.setName(cvLayer->name);
-    layer.setType(kOpenCVLayersType);
-    layer.getParameters()["impl"] = (size_t)cvLayer.get();
-    layer.getParameters()["outputs"] = shapesToStr(outputs);
-    layer.getParameters()["internals"] = shapesToStr(internals);
-    layer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
-    layer.setOutputPorts(std::vector<InferenceEngine::Port>(outputs.size()));
-}
-
-static std::vector<Ptr<InfEngineBackendWrapper> >
-infEngineWrappers(const std::vector<Ptr<BackendWrapper> >& ptrs)
-{
-    std::vector<Ptr<InfEngineBackendWrapper> > wrappers(ptrs.size());
-    for (int i = 0; i < ptrs.size(); ++i)
-    {
-        CV_Assert(!ptrs[i].empty());
-        wrappers[i] = ptrs[i].dynamicCast<InfEngineBackendWrapper>();
-        CV_Assert(!wrappers[i].empty());
-    }
-    return wrappers;
-}
-
-InfEngineBackendNet::InfEngineBackendNet() : netBuilder("")
-{
-    hasNetOwner = false;
-    device_name = "CPU";
-}
-
-InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net) : netBuilder(""), cnn(net)
-{
-    hasNetOwner = true;
-    device_name = "CPU";
-}
-
-void InfEngineBackendNet::connect(const std::vector<Ptr<BackendWrapper> >& inputs,
-                                  const std::vector<Ptr<BackendWrapper> >& outputs,
-                                  const std::string& layerName)
-{
-    std::vector<Ptr<InfEngineBackendWrapper> > inpWrappers = infEngineWrappers(inputs);
-    std::map<std::string, int>::iterator it = layers.find(layerName);
-    CV_Assert(it != layers.end());
-
-    const int layerId = it->second;
-    for (size_t i = 0; i < inpWrappers.size(); ++i)
-    {
-        const auto& inp = inpWrappers[i];
-        const std::string& inpName = inp->dataPtr->getName();
-
-        std::string inpLayerName = inpName;
-        size_t inpPortId = inpName.rfind('.');
-        if (inpPortId != std::string::npos)
-        {
-            std::string portIdStr = inpName.substr(inpPortId + 1);
-            if (std::all_of(portIdStr.begin(), portIdStr.end(), ::isdigit))
-            {
-                inpLayerName = inpName.substr(0, inpPortId);
-                inpPortId = atoi(portIdStr.c_str());
-            }
-            else
-                inpPortId = 0;
-        }
-        else
-            inpPortId = 0;
-
-        int inpId;
-        it = layers.find(inpLayerName);
-        if (it == layers.end())
-        {
-            InferenceEngine::Builder::InputLayer inpLayer(!inpLayerName.empty() ? inpLayerName : kDefaultInpLayerName);
-            std::vector<size_t> shape(inp->blob->getTensorDesc().getDims());
-            inpLayer.setPort(InferenceEngine::Port(shape));
-            inpId = netBuilder.addLayer(inpLayer);
-
-            layers.insert({inpName, inpId});
-        }
-        else
-            inpId = it->second;
-
-        netBuilder.connect({(size_t)inpId, inpPortId}, {(size_t)layerId, i});
-        unconnectedPorts.erase({inpId, inpPortId});
-    }
-    CV_Assert(!outputs.empty());
-    for (int i = 0; i < outputs.size(); ++i)
-    {
-        InferenceEngine::DataPtr dataPtr = infEngineDataNode(outputs[i]);
-        std::string outputName = outputs.size() > 1 ? (layerName + "." + std::to_string(i)) : layerName;
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-        dataPtr->name = outputName;
-#else
-        dataPtr->setName(outputName);
-#endif
-    }
-}
-
-void InfEngineBackendNet::init(Target targetId)
-{
-    if (!hasNetOwner)
-    {
-        CV_Assert(!unconnectedPorts.empty());
-        for (const auto& port : unconnectedPorts)
-        {
-            InferenceEngine::Builder::OutputLayer outLayer("myconv1");
-#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
-            // Inference Engine determines network precision by ports.
-            InferenceEngine::Precision p = (targetId == DNN_TARGET_MYRIAD ||
-                                            targetId == DNN_TARGET_HDDL ||
-                                            targetId == DNN_TARGET_OPENCL_FP16) ?
-                                           InferenceEngine::Precision::FP16 :
-                                           InferenceEngine::Precision::FP32;
-            outLayer.setPort(InferenceEngine::Port({}, p));
-#endif
-            netBuilder.addLayer({InferenceEngine::PortInfo(port.first, port.second)}, outLayer);
-        }
-        netBuilder.getContext().addShapeInferImpl(kOpenCVLayersType,
-                            std::make_shared<InfEngineCustomLayerShapeInfer>());
-        cnn = InferenceEngine::CNNNetwork(InferenceEngine::Builder::convertToICNNNetwork(netBuilder.build()));
-    }
-
-    switch (targetId)
-    {
-        case DNN_TARGET_CPU:
-            device_name = "CPU";
-            break;
-        case DNN_TARGET_OPENCL:
-        case DNN_TARGET_OPENCL_FP16:
-            device_name = "GPU";
-            break;
-        case DNN_TARGET_MYRIAD:
-            device_name = "MYRIAD";
-            break;
-        case DNN_TARGET_HDDL:
-            device_name = "HDDL";
-            break;
-        case DNN_TARGET_FPGA:
-            device_name = "FPGA";
-            break;
-        default:
-            CV_Error(Error::StsNotImplemented, "Unknown target");
-    };
-
-    for (const auto& name : requestedOutputs)
-    {
-        cnn.addOutput(name);
-    }
-
-    for (const auto& it : cnn.getInputsInfo())
-    {
-        const std::string& name = it.first;
-        auto blobIt = allBlobs.find(name);
-        CV_Assert(blobIt != allBlobs.end());
-        it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision());
-    }
-    for (const auto& it : cnn.getOutputsInfo())
-    {
-        const std::string& name = it.first;
-        auto blobIt = allBlobs.find(name);
-        CV_Assert(blobIt != allBlobs.end());
-        it.second->setPrecision(blobIt->second->getTensorDesc().getPrecision());  // Should be always FP32
-    }
-
-    initPlugin(cnn);
-}
-
-void InfEngineBackendNet::addLayer(InferenceEngine::Builder::Layer& layer)
-{
-#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
-    // Add weights to network and connect them after input blobs.
-    std::map<std::string, InferenceEngine::Parameter>& params = layer.getParameters();
-    std::vector<int> blobsIds;
-    std::vector<int> portIds;
-    for (const std::string& name : {"weights", "biases"})
-    {
-        bool asInput = false;
-        int portId = 0;
-        for (int i = 0; i < layer.getInputPorts().size(); ++i)
-        {
-            const auto& port = layer.getInputPorts()[i];
-            auto it = port.getParameters().find("type");
-            if (it != port.getParameters().end() && it->second == name)
-            {
-                portId = i;
-                asInput = true;
-                break;
-            }
-        }
-
-        if (!asInput)
-            continue;
-
-        auto it = params.find(name);
-        if (it != params.end())
-        {
-            InferenceEngine::Blob::Ptr blob = it->second.as<InferenceEngine::Blob::Ptr>();
-            params.erase(it);
-            int blobId = netBuilder.addLayer(InferenceEngine::Builder::ConstLayer(name).setData(blob));
-            blobsIds.push_back(blobId);
-            portIds.push_back(portId);
-        }
-    }
-#endif
-
-    int id = netBuilder.addLayer(layer);
-    const std::string& layerName = layer.getName();
-
-    CV_Assert(layers.insert({layerName, id}).second);
-    for (int i = 0; i < layer.getOutputPorts().size(); ++i)
-        unconnectedPorts.insert({id, i});
-
-#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
-    // By default, all the weights are connected to last ports ids.
-    for (int i = 0; i < blobsIds.size(); ++i)
-    {
-        netBuilder.connect((size_t)blobsIds[i], {(size_t)id, (size_t)portIds[i]});
-    }
-#endif
-}
-
-void InfEngineBackendNet::addOutput(const std::string& name)
-{
-    requestedOutputs.push_back(name);
-}
-
-static InferenceEngine::Layout estimateLayout(const Mat& m)
-{
-    if (m.dims == 4)
-        return InferenceEngine::Layout::NCHW;
-    else if (m.dims == 2)
-        return InferenceEngine::Layout::NC;
-    else
-        return InferenceEngine::Layout::ANY;
-}
-
-static InferenceEngine::DataPtr wrapToInfEngineDataNode(const Mat& m, const std::string& name = "")
-{
-    std::vector<size_t> shape = getShape<size_t>(m);
-    if (m.type() == CV_32F)
-        return InferenceEngine::DataPtr(new InferenceEngine::Data(name,
-               {InferenceEngine::Precision::FP32, shape, estimateLayout(m)}));
-    else if (m.type() == CV_8U)
-        return InferenceEngine::DataPtr(new InferenceEngine::Data(name,
-               {InferenceEngine::Precision::U8, shape, estimateLayout(m)}));
-    else
-        CV_Error(Error::StsNotImplemented, format("Unsupported data type %d", m.type()));
-}
-
-InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape,
-                                               InferenceEngine::Layout layout)
-{
-    if (m.type() == CV_32F)
-        return InferenceEngine::make_shared_blob<float>(
-               {InferenceEngine::Precision::FP32, shape, layout}, (float*)m.data);
-    else if (m.type() == CV_8U)
-        return InferenceEngine::make_shared_blob<uint8_t>(
-               {InferenceEngine::Precision::U8, shape, layout}, (uint8_t*)m.data);
-    else
-        CV_Error(Error::StsNotImplemented, format("Unsupported data type %d", m.type()));
-}
-
-InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, InferenceEngine::Layout layout)
-{
-    std::vector<size_t> shape = getShape<size_t>(m);
-    return wrapToInfEngineBlob(m, shape, layout);
-}
-
-InferenceEngine::Blob::Ptr cloneBlob(const InferenceEngine::Blob::Ptr& blob)
-{
-    InferenceEngine::Blob::Ptr copy;
-    auto description = blob->getTensorDesc();
-    InferenceEngine::Precision precision = description.getPrecision();
-    if (precision == InferenceEngine::Precision::FP32)
-    {
-        copy = InferenceEngine::make_shared_blob<float>(description);
-    }
-    else if (precision == InferenceEngine::Precision::U8)
-    {
-        copy = InferenceEngine::make_shared_blob<uint8_t>(description);
-    }
-    else
-        CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
-    copy->allocate();
-    return copy;
-}
-
-InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr)
-{
-    CV_Assert(!ptr.empty());
-    Ptr<InfEngineBackendWrapper> p = ptr.dynamicCast<InfEngineBackendWrapper>();
-    CV_Assert(!p.empty());
-    return p->dataPtr;
-}
-
-InfEngineBackendWrapper::InfEngineBackendWrapper(int targetId, const cv::Mat& m)
-    : BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, targetId)
-{
-    dataPtr = wrapToInfEngineDataNode(m);
-    blob = wrapToInfEngineBlob(m, estimateLayout(m));
-}
-
-InfEngineBackendWrapper::InfEngineBackendWrapper(Ptr<BackendWrapper> wrapper)
-    : BackendWrapper(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, wrapper->targetId)
-{
-    Ptr<InfEngineBackendWrapper> ieWrapper = wrapper.dynamicCast<InfEngineBackendWrapper>();
-    CV_Assert(!ieWrapper.empty());
-    InferenceEngine::DataPtr srcData = ieWrapper->dataPtr;
-
-    dataPtr = InferenceEngine::DataPtr(new InferenceEngine::Data(srcData->getName(), srcData->getTensorDesc()));
-    blob = ieWrapper->blob;
-}
-
-Ptr<BackendWrapper> InfEngineBackendWrapper::create(Ptr<BackendWrapper> wrapper)
-{
-    return Ptr<BackendWrapper>(new InfEngineBackendWrapper(wrapper));
-}
-
-InfEngineBackendWrapper::~InfEngineBackendWrapper()
-{
-
-}
-
-void InfEngineBackendWrapper::copyToHost()
-{
-
-}
-
-void InfEngineBackendWrapper::setHostDirty()
-{
-
-}
-
-#endif // HAVE_DNN_IE_NN_BUILDER_2019
-
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-static std::map<std::string, InferenceEngine::InferenceEnginePluginPtr>& getSharedPlugins()
-{
-    static std::map<std::string, InferenceEngine::InferenceEnginePluginPtr> sharedPlugins;
-    return sharedPlugins;
-}
-#else
 static bool init_IE_plugins()
 {
     // load and hold IE plugins
@@ -653,7 +116,7 @@ InferenceEngine::Core& getCore(const std::string& id)
             : create_IE_Core_instance(id);
     return core;
 }
-#endif
+
 
 static bool detectArmPlugin_()
 {
@@ -672,10 +135,10 @@ static bool detectArmPlugin_()
 }
 
 #if !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
-static bool detectMyriadX_(std::string device)
+static bool detectMyriadX_(const std::string& device)
 {
     AutoLock lock(getInitializationMutex());
-#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R3)
+
     // Lightweight detection
     InferenceEngine::Core& ie = getCore(device);
     const std::vector<std::string> devices = ie.GetAvailableDevices();
@@ -689,481 +152,22 @@ static bool detectMyriadX_(std::string device)
         }
     }
     return false;
-#else
-    InferenceEngine::Builder::Network builder("");
-    InferenceEngine::idx_t inpId = builder.addLayer(
-                                   InferenceEngine::Builder::InputLayer().setPort(InferenceEngine::Port({1})));
-
-#if INF_ENGINE_RELEASE <= 2018050000
-    InferenceEngine::idx_t clampId;
-    {
-        InferenceEngine::Builder::Layer l = InferenceEngine::Builder::ClampLayer();
-        auto& blobs = l.getConstantData();
-        auto blob = InferenceEngine::make_shared_blob<int16_t>(
-                        InferenceEngine::Precision::FP16,
-                        InferenceEngine::Layout::C, {1});
-        blob->allocate();
-        blobs[""] = blob;
-        clampId = builder.addLayer({inpId}, l);
-    }
-    builder.addLayer({InferenceEngine::PortInfo(clampId)}, InferenceEngine::Builder::OutputLayer());
-#else
-
-    InferenceEngine::idx_t clampId = builder.addLayer({inpId}, InferenceEngine::Builder::ClampLayer());
-    builder.addLayer({InferenceEngine::PortInfo(clampId)},
-                      InferenceEngine::Builder::OutputLayer().setPort(InferenceEngine::Port({},
-                      InferenceEngine::Precision::FP16)));
-#endif
-
-    InferenceEngine::CNNNetwork cnn = InferenceEngine::CNNNetwork(
-                                      InferenceEngine::Builder::convertToICNNNetwork(builder.build()));
-
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-    InferenceEngine::InferenceEnginePluginPtr enginePtr;
-    {
-        auto& sharedPlugins = getSharedPlugins();
-        auto pluginIt = sharedPlugins.find(device);
-        if (pluginIt != sharedPlugins.end()) {
-            enginePtr = pluginIt->second;
-        } else {
-            auto dispatcher = InferenceEngine::PluginDispatcher({""});
-            enginePtr = dispatcher.getPluginByDevice(device);
-            sharedPlugins[device] = enginePtr;
-        }
-    }
-    auto plugin = InferenceEngine::InferencePlugin(enginePtr);
-    try
-    {
-        auto netExec = plugin.LoadNetwork(cnn, {{"VPU_PLATFORM", "VPU_2480"}});
-#else
-    try
-    {
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
-        auto netExec = getCore(device).LoadNetwork(cnn, device, {{"VPU_PLATFORM", "VPU_2480"}});
-#else
-        auto netExec = getCore(device).LoadNetwork(cnn, device, {{"VPU_MYRIAD_PLATFORM", "VPU_MYRIAD_2480"}});
-#endif
-#endif
-        auto infRequest = netExec.CreateInferRequest();
-    } catch(...) {
-        return false;
-    }
-    return true;
-#endif
 }
 #endif  // !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
 
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-
-void InfEngineBackendNet::initPlugin(InferenceEngine::CNNNetwork& net)
-{
-    CV_Assert(!isInitialized());
-
-    try
-    {
-        AutoLock lock(getInitializationMutex());
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-        auto& sharedPlugins = getSharedPlugins();
-        auto pluginIt = sharedPlugins.find(device_name);
-        if (pluginIt != sharedPlugins.end())
-        {
-            enginePtr = pluginIt->second;
-        }
-        else
-#else
-        InferenceEngine::Core& ie = getCore(device_name);
-#endif
-        {
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-            auto dispatcher = InferenceEngine::PluginDispatcher({""});
-            if (device_name == "FPGA")
-                enginePtr = dispatcher.getPluginByDevice("HETERO:FPGA,CPU");
-            else
-                enginePtr = dispatcher.getPluginByDevice(device_name);
-            sharedPlugins[device_name] = enginePtr;
-#else
-            isInit = true;
-#endif
-            std::vector<std::string> candidates;
-            std::string param_pluginPath = utils::getConfigurationParameterString("OPENCV_DNN_IE_EXTRA_PLUGIN_PATH", "");
-            if (!param_pluginPath.empty())
-            {
-                candidates.push_back(param_pluginPath);
-            }
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R3)
-            if (device_name == "CPU" || device_name == "FPGA")
-            {
-                std::string suffixes[] = {"_avx2", "_sse4", ""};
-                bool haveFeature[] = {
-                    checkHardwareSupport(CPU_AVX2),
-                    checkHardwareSupport(CPU_SSE4_2),
-                    true
-                };
-                for (int i = 0; i < 3; ++i)
-                {
-                    if (!haveFeature[i])
-                        continue;
-#ifdef _WIN32
-                    candidates.push_back("cpu_extension" + suffixes[i] + ".dll");
-#elif defined(__APPLE__)
-                    candidates.push_back("libcpu_extension" + suffixes[i] + ".so");  // built as loadable module
-                    candidates.push_back("libcpu_extension" + suffixes[i] + ".dylib");  // built as shared library
-#else
-                    candidates.push_back("libcpu_extension" + suffixes[i] + ".so");
-#endif  // _WIN32
-                }
-            }
-#endif
-            bool found = false;
-            for (size_t i = 0; i != candidates.size(); ++i)
-            {
-                const std::string& libName = candidates[i];
-                try
-                {
-                    InferenceEngine::IExtensionPtr extension =
-                        InferenceEngine::make_so_pointer<InferenceEngine::IExtension>(libName);
-
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-                    enginePtr->AddExtension(extension, 0);
-#else
-                    ie.AddExtension(extension, "CPU");
-#endif
-                    CV_LOG_INFO(NULL, "DNN-IE: Loaded extension plugin: " << libName);
-                    found = true;
-                    break;
-                }
-                catch(...) {}
-            }
-            if (!found && !candidates.empty())
-            {
-                CV_LOG_WARNING(NULL, "DNN-IE: Can't load extension plugin (extra layers for some networks). Specify path via OPENCV_DNN_IE_EXTRA_PLUGIN_PATH parameter");
-            }
-            // Some of networks can work without a library of extra layers.
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2019R1)
-            // OpenCV fallbacks as extensions.
-            try
-            {
-                ie.AddExtension(std::make_shared<InfEngineExtension>(), "CPU");
-            }
-            catch(const std::exception& e)
-            {
-                CV_LOG_INFO(NULL, "DNN-IE: Can't register OpenCV custom layers extension: " << e.what());
-            }
-#endif
-            // Limit the number of CPU threads.
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-#ifndef _WIN32
-            enginePtr->SetConfig({{
-                InferenceEngine::PluginConfigParams::KEY_CPU_THREADS_NUM, format("%d", getNumThreads()),
-            }}, 0);
-#endif  // _WIN32
-#else
-            if (device_name == "CPU")
-                ie.SetConfig({{
-                    InferenceEngine::PluginConfigParams::KEY_CPU_THREADS_NUM, format("%d", getNumThreads()),
-                }}, device_name);
-#endif
-        }
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-        plugin = InferenceEngine::InferencePlugin(enginePtr);
-        netExec = plugin.LoadNetwork(net, {});
-#else
-        bool isHetero = false;
-        if (device_name != "CPU")
-        {
-            isHetero = device_name == "FPGA";
-            for (auto& layer : net)
-            {
-                if (layer->type == kOpenCVLayersType)
-                {
-                    isHetero = true;
-#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2019R3)
-                    // Not sure about lower versions but in 2019R3 we do not need this
-                    layer->affinity = "CPU";
-                }
-                else
-                {
-                    layer->affinity = device_name;
-#endif
-                }
-            }
-        }
-        if (isHetero)
-            netExec = ie.LoadNetwork(net, "HETERO:" + device_name + ",CPU");
-        else
-            netExec = ie.LoadNetwork(net, device_name);
-#endif
-    }
-    catch (const std::exception& ex)
-    {
-        CV_Error(Error::StsError, format("Failed to initialize Inference Engine backend (device = %s): %s", device_name.c_str(), ex.what()));
-    }
-}
-
-bool InfEngineBackendNet::isInitialized()
-{
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-    return (bool)enginePtr;
-#else
-    return isInit;
-#endif
-}
-
-void InfEngineBackendNet::reset()
-{
-    allBlobs.clear();
-    infRequests.clear();
-    isInit = false;
-}
-
-void InfEngineBackendNet::addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs)
-{
-    auto wrappers = infEngineWrappers(ptrs);
-    for (const auto& wrapper : wrappers)
-    {
-        std::string name = wrapper->dataPtr->getName();
-        name = name.empty() ? kDefaultInpLayerName : name;
-        allBlobs.insert({name, wrapper->blob});
-    }
-}
-
-void InfEngineBackendNet::InfEngineReqWrapper::makePromises(const std::vector<Ptr<BackendWrapper> >& outsWrappers)
-{
-    auto outs = infEngineWrappers(outsWrappers);
-    outProms.clear();
-    outProms.resize(outs.size());
-    outsNames.resize(outs.size());
-    for (int i = 0; i < outs.size(); ++i)
-    {
-        outs[i]->futureMat = outProms[i].getArrayResult();
-        outsNames[i] = outs[i]->dataPtr->getName();
-    }
-}
-
-void InfEngineBackendNet::forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
-                                  bool isAsync)
-{
-    CV_LOG_DEBUG(NULL, "InfEngineBackendNet::forward(" << (isAsync ? "async" : "sync") << ")");
-    // Look for finished requests.
-    Ptr<InfEngineReqWrapper> reqWrapper;
-    for (auto& wrapper : infRequests)
-    {
-        if (wrapper->isReady)
-        {
-            reqWrapper = wrapper;
-            break;
-        }
-    }
-    if (reqWrapper.empty())
-    {
-        reqWrapper = Ptr<InfEngineReqWrapper>(new InfEngineReqWrapper());
-        try
-        {
-            reqWrapper->req = netExec.CreateInferRequest();
-        }
-        catch (const std::exception& ex)
-        {
-            CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what()));
-        }
-        infRequests.push_back(reqWrapper);
-
-        InferenceEngine::BlobMap inpBlobs, outBlobs;
-        for (const auto& it : cnn.getInputsInfo())
-        {
-            const std::string& name = it.first;
-            auto blobIt = allBlobs.find(name);
-            CV_Assert(blobIt != allBlobs.end());
-            inpBlobs[name] = isAsync ? cloneBlob(blobIt->second) : blobIt->second;
-        }
-        for (const auto& it : cnn.getOutputsInfo())
-        {
-            const std::string& name = it.first;
-            auto blobIt = allBlobs.find(name);
-            CV_Assert(blobIt != allBlobs.end());
-            outBlobs[name] = isAsync ? cloneBlob(blobIt->second) : blobIt->second;
-        }
-        reqWrapper->req.SetInput(inpBlobs);
-        reqWrapper->req.SetOutput(outBlobs);
-
-        InferenceEngine::IInferRequest::Ptr infRequestPtr = reqWrapper->req;
-        infRequestPtr->SetUserData(reqWrapper.get(), 0);
-
-        infRequestPtr->SetCompletionCallback(
-            [](InferenceEngine::IInferRequest::Ptr request, InferenceEngine::StatusCode status)
-            {
-                CV_LOG_DEBUG(NULL, "DNN(IE): completionCallback(" << (int)status << ")");
-
-                InfEngineReqWrapper* wrapper;
-                request->GetUserData((void**)&wrapper, 0);
-                CV_Assert(wrapper && "Internal error");
-
-                size_t processedOutputs = 0;
-                try
-                {
-                    for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs)
-                    {
-                        const std::string& name = wrapper->outsNames[processedOutputs];
-                        Mat m = infEngineBlobToMat(wrapper->req.GetBlob(name));
-
-                        try
-                        {
-                            CV_Assert(status == InferenceEngine::StatusCode::OK);
-                            wrapper->outProms[processedOutputs].setValue(m.clone());
-                        }
-                        catch (...)
-                        {
-                            try {
-                                wrapper->outProms[processedOutputs].setException(std::current_exception());
-                            } catch(...) {
-                                CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
-                            }
-                        }
-                    }
-                }
-                catch (...)
-                {
-                    std::exception_ptr e = std::current_exception();
-                    for (; processedOutputs < wrapper->outProms.size(); ++processedOutputs)
-                    {
-                        try {
-                            wrapper->outProms[processedOutputs].setException(e);
-                        } catch(...) {
-                            CV_LOG_ERROR(NULL, "DNN: Exception occurred during async inference exception propagation");
-                        }
-                    }
-                }
-                wrapper->isReady = true;
-            }
-        );
-    }
-    if (isAsync)
-    {
-        // Copy actual data to infer request's input blobs.
-        for (const auto& it : cnn.getInputsInfo())
-        {
-            const std::string& name = it.first;
-            auto blobIt = allBlobs.find(name);
-            Mat srcMat = infEngineBlobToMat(blobIt->second);
-            Mat dstMat = infEngineBlobToMat(reqWrapper->req.GetBlob(name));
-            srcMat.copyTo(dstMat);
-        }
-
-        // Set promises to output blobs wrappers.
-        reqWrapper->makePromises(outBlobsWrappers);
-
-        reqWrapper->isReady = false;
-        reqWrapper->req.StartAsync();
-    }
-    else
-    {
-        reqWrapper->req.Infer();
-    }
-}
-
-bool InfEngineBackendLayer::getMemoryShapes(const std::vector<MatShape> &inputs,
-                                            const int requiredOutputs,
-                                            std::vector<MatShape> &outputs,
-                                            std::vector<MatShape> &internals) const
-{
-    InferenceEngine::ICNNNetwork::InputShapes inShapes = t_net.getInputShapes();
-    InferenceEngine::ICNNNetwork::InputShapes::iterator itr;
-    bool equal_flag = true;
-    size_t i = 0;
-    for (itr = inShapes.begin(); itr != inShapes.end(); ++itr)
-    {
-        InferenceEngine::SizeVector currentInShape(inputs[i].begin(), inputs[i].end());
-        if (itr->second != currentInShape)
-        {
-            itr->second = currentInShape;
-            equal_flag = false;
-        }
-        i++;
-    }
-
-    if (!equal_flag)
-    {
-        InferenceEngine::CNNNetwork curr_t_net(t_net);
-        curr_t_net.reshape(inShapes);
-    }
-    std::vector<size_t> dims = t_net.getOutputsInfo()[name]->getDims();
-    outputs.push_back(MatShape(dims.begin(), dims.end()));
-    return false;
-}
-
-bool InfEngineBackendLayer::supportBackend(int backendId)
-{
-    CV_LOG_DEBUG(NULL, "InfEngineBackendLayer::supportBackend(" << backendId << ")");
-    return backendId == DNN_BACKEND_DEFAULT ||
-           (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019);
-}
-
-void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,
-                                    OutputArrayOfArrays internals)
-{
-    CV_Error(Error::StsInternal, "Choose Inference Engine as a preferable backend.");
-}
-
-InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob)
-{
-    auto halfs = InferenceEngine::make_shared_blob<int16_t>({
-                     InferenceEngine::Precision::FP16, blob->getTensorDesc().getDims(),
-                     blob->getTensorDesc().getLayout()
-                 });
-    halfs->allocate();
-    Mat floatsData(1, blob->size(), CV_32F, blob->buffer());
-    Mat halfsData(1, blob->size(), CV_16SC1, halfs->buffer());
-    convertFp16(floatsData, halfsData);
-    return halfs;
-}
-
-void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data,
-                     InferenceEngine::Builder::Layer& l)
-{
-#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
-    l.getParameters()[name] = data;
-#else
-    l.addConstantData(name, data);
-#endif
-}
-
-#endif // HAVE_DNN_IE_NN_BUILDER_2019
-
 #endif  // HAVE_INF_ENGINE
 
-bool haveInfEngine()
-{
-#ifdef HAVE_INF_ENGINE
-    return true;
-#else
-    return false;
-#endif  // HAVE_INF_ENGINE
-}
-
-void forwardInfEngine(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
-                      Ptr<BackendNode>& node, bool isAsync)
-{
-    CV_Assert(haveInfEngine());
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-    CV_Assert(!node.empty());
-    Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
-    CV_Assert(!ieNode.empty());
-    ieNode->net->forward(outBlobsWrappers, isAsync);
-#else
-    CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
-#endif  // HAVE_INF_ENGINE
-}
 
 CV__DNN_INLINE_NS_BEGIN
 
 void resetMyriadDevice()
 {
 #ifdef HAVE_INF_ENGINE
+    CV_LOG_INFO(NULL, "DNN: Unregistering both 'MYRIAD' and 'HETERO:MYRIAD,CPU' plugins");
+
     AutoLock lock(getInitializationMutex());
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-    getSharedPlugins().erase("MYRIAD");
-#else
-    // Unregister both "MYRIAD" and "HETERO:MYRIAD,CPU" plugins
+
     InferenceEngine::Core& ie = getCore("MYRIAD");
     try
     {
@@ -1171,18 +175,16 @@ void resetMyriadDevice()
         ie.UnregisterPlugin("HETERO");
     }
     catch (...) {}
-#endif
 #endif  // HAVE_INF_ENGINE
 }
 
 void releaseHDDLPlugin()
 {
 #ifdef HAVE_INF_ENGINE
+    CV_LOG_INFO(NULL, "DNN: Unregistering both 'HDDL' and 'HETERO:HDDL,CPU' plugins");
+
     AutoLock lock(getInitializationMutex());
-#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
-    getSharedPlugins().erase("HDDL");
-#else
-    // Unregister both "HDDL" and "HETERO:HDDL,CPU" plugins
+
     InferenceEngine::Core& ie = getCore("HDDL");
     try
     {
@@ -1190,7 +192,6 @@ void releaseHDDLPlugin()
         ie.UnregisterPlugin("HETERO");
     }
     catch (...) {}
-#endif
 #endif  // HAVE_INF_ENGINE
 }
 
diff --git a/modules/dnn/src/op_inf_engine.hpp b/modules/dnn/src/op_inf_engine.hpp
index ab2f161eaf..ed1323d7dd 100644
--- a/modules/dnn/src/op_inf_engine.hpp
+++ b/modules/dnn/src/op_inf_engine.hpp
@@ -48,37 +48,16 @@
 #pragma GCC diagnostic ignored "-Wsuggest-override"
 #endif
 
-#if defined(HAVE_DNN_IE_NN_BUILDER_2019) || INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2020_4)
-//#define INFERENCE_ENGINE_DEPRECATED  // turn off deprecation warnings from IE
-//there is no way to suppress warnings from IE only at this moment, so we are forced to suppress warnings globally
-#if defined(__GNUC__)
-#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-#endif
-#ifdef _MSC_VER
-#pragma warning(disable: 4996)  // was declared deprecated
-#endif
-#endif
-
-#if defined(__GNUC__) && INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_1)
-#pragma GCC visibility push(default)
-#endif
-
 #include <inference_engine.hpp>
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-#include <ie_builders.hpp>
-#endif
-
-#if defined(__GNUC__) && INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_1)
-#pragma GCC visibility pop
-#endif
-
 #if defined(__GNUC__) && __GNUC__ >= 5
 //#pragma GCC diagnostic pop
 #endif
 
 #endif  // HAVE_INF_ENGINE
 
+#define CV_ERROR_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 do { CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support (legacy API is not supported anymore)"); } while (0)
+
 namespace cv { namespace dnn {
 
 #ifdef HAVE_INF_ENGINE
@@ -90,167 +69,6 @@ Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
 void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
                           std::vector<Mat>& mats);
 
-#ifdef HAVE_DNN_IE_NN_BUILDER_2019
-
-class InfEngineBackendNet
-{
-public:
-    InfEngineBackendNet();
-
-    InfEngineBackendNet(InferenceEngine::CNNNetwork& net);
-
-    void addLayer(InferenceEngine::Builder::Layer& layer);
-
-    void addOutput(const std::string& name);
-
-    void connect(const std::vector<Ptr<BackendWrapper> >& inputs,
-                 const std::vector<Ptr<BackendWrapper> >& outputs,
-                 const std::string& layerName);
-
-    bool isInitialized();
-
-    void init(Target targetId);
-
-    void forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
-                 bool isAsync);
-
-    void initPlugin(InferenceEngine::CNNNetwork& net);
-
-    void addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs);
-
-    void reset();
-
-private:
-    InferenceEngine::Builder::Network netBuilder;
-
-    InferenceEngine::ExecutableNetwork netExec;
-    InferenceEngine::BlobMap allBlobs;
-    std::string device_name;
-#if INF_ENGINE_VER_MAJOR_LE(2019010000)
-    InferenceEngine::InferenceEnginePluginPtr enginePtr;
-    InferenceEngine::InferencePlugin plugin;
-#else
-    bool isInit = false;
-#endif
-
-    struct InfEngineReqWrapper
-    {
-        InfEngineReqWrapper() : isReady(true) {}
-
-        void makePromises(const std::vector<Ptr<BackendWrapper> >& outs);
-
-        InferenceEngine::InferRequest req;
-        std::vector<cv::AsyncPromise> outProms;
-        std::vector<std::string> outsNames;
-        bool isReady;
-    };
-
-    std::vector<Ptr<InfEngineReqWrapper> > infRequests;
-
-    InferenceEngine::CNNNetwork cnn;
-    bool hasNetOwner;
-
-    std::map<std::string, int> layers;
-    std::vector<std::string> requestedOutputs;
-
-    std::set<std::pair<int, int> > unconnectedPorts;
-};
-
-class InfEngineBackendNode : public BackendNode
-{
-public:
-    InfEngineBackendNode(const InferenceEngine::Builder::Layer& layer);
-
-    InfEngineBackendNode(Ptr<Layer>& layer, std::vector<Mat*>& inputs,
-                         std::vector<Mat>& outputs, std::vector<Mat>& internals);
-
-    void connect(std::vector<Ptr<BackendWrapper> >& inputs,
-                 std::vector<Ptr<BackendWrapper> >& outputs);
-
-    // Inference Engine network object that allows to obtain the outputs of this layer.
-    InferenceEngine::Builder::Layer layer;
-    Ptr<InfEngineBackendNet> net;
-    // CPU fallback in case of unsupported Inference Engine layer.
-    Ptr<dnn::Layer> cvLayer;
-};
-
-class InfEngineBackendWrapper : public BackendWrapper
-{
-public:
-    InfEngineBackendWrapper(int targetId, const Mat& m);
-
-    InfEngineBackendWrapper(Ptr<BackendWrapper> wrapper);
-
-    ~InfEngineBackendWrapper();
-
-    static Ptr<BackendWrapper> create(Ptr<BackendWrapper> wrapper);
-
-    virtual void copyToHost() CV_OVERRIDE;
-
-    virtual void setHostDirty() CV_OVERRIDE;
-
-    InferenceEngine::DataPtr dataPtr;
-    InferenceEngine::Blob::Ptr blob;
-    AsyncArray futureMat;
-};
-
-InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, InferenceEngine::Layout layout = InferenceEngine::Layout::ANY);
-
-InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<size_t>& shape, InferenceEngine::Layout layout);
-
-InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr);
-
-// Convert Inference Engine blob with FP32 precision to FP16 precision.
-// Allocates memory for a new blob.
-InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
-
-void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data, InferenceEngine::Builder::Layer& l);
-
-// This is a fake class to run networks from Model Optimizer. Objects of that
-// class simulate responses of layers are imported by OpenCV and supported by
-// Inference Engine. The main difference is that they do not perform forward pass.
-class InfEngineBackendLayer : public Layer
-{
-public:
-    InfEngineBackendLayer(const InferenceEngine::CNNNetwork &t_net_) : t_net(t_net_) {};
-
-    virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
-                                 const int requiredOutputs,
-                                 std::vector<MatShape> &outputs,
-                                 std::vector<MatShape> &internals) const CV_OVERRIDE;
-
-    virtual void forward(InputArrayOfArrays inputs, OutputArrayOfArrays outputs,
-                         OutputArrayOfArrays internals) CV_OVERRIDE;
-
-    virtual bool supportBackend(int backendId) CV_OVERRIDE;
-
-private:
-    InferenceEngine::CNNNetwork t_net;
-};
-
-
-class InfEngineExtension : public InferenceEngine::IExtension
-{
-public:
-#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2)
-    virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {}
-#endif
-    virtual void Unload() noexcept {}
-    virtual void Release() noexcept {}
-    virtual void GetVersion(const InferenceEngine::Version*&) const noexcept {}
-
-    virtual InferenceEngine::StatusCode getPrimitiveTypes(char**&, unsigned int&,
-                                                          InferenceEngine::ResponseDesc*) noexcept
-    {
-        return InferenceEngine::StatusCode::OK;
-    }
-
-    InferenceEngine::StatusCode getFactoryFor(InferenceEngine::ILayerImplFactory*& factory,
-                                              const InferenceEngine::CNNLayer* cnnLayer,
-                                              InferenceEngine::ResponseDesc* resp) noexcept;
-};
-
-#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 CV__DNN_INLINE_NS_BEGIN
@@ -273,14 +91,8 @@ static inline std::vector<T> getShape(const Mat& mat)
     return result;
 }
 
-
 #endif  // HAVE_INF_ENGINE
 
-bool haveInfEngine();
-
-void forwardInfEngine(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
-                      Ptr<BackendNode>& node, bool isAsync);
-
 }}  // namespace dnn, namespace cv
 
 #endif  // __OPENCV_DNN_OP_INF_ENGINE_HPP__
diff --git a/modules/dnn/test/test_common.impl.hpp b/modules/dnn/test/test_common.impl.hpp
index 9de1ac6821..747dc02ce6 100644
--- a/modules/dnn/test/test_common.impl.hpp
+++ b/modules/dnn/test/test_common.impl.hpp
@@ -337,16 +337,6 @@ testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTarget
     std::vector< tuple<Backend, Target> > targets;
     std::vector< Target > available;
 
-    {
-        available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019);
-        for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
-        {
-            if ((*i == DNN_TARGET_MYRIAD || *i == DNN_TARGET_HDDL) && !withVPU)
-                continue;
-            targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, *i));
-        }
-    }
-
     {
         available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
         for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
diff --git a/modules/dnn/test/test_ie_models.cpp b/modules/dnn/test/test_ie_models.cpp
index 2846f9ae76..0fe19db5e9 100644
--- a/modules/dnn/test/test_ie_models.cpp
+++ b/modules/dnn/test/test_ie_models.cpp
@@ -371,17 +371,17 @@ TEST_P(DNNTestOpenVINO, models)
             || modelName == "person-vehicle-bike-detection-2004"  // 2021.4+: ncDeviceOpen:1013 Failed to find booted device after boot
         )
     )
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
     if (targetId == DNN_TARGET_OPENCL && (false
             || modelName == "face-detection-0106"  // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported
         )
     )
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
     if (targetId == DNN_TARGET_OPENCL_FP16 && (false
             || modelName == "face-detection-0106"  // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported
         )
     )
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
 #endif
 
 #if INF_ENGINE_VER_MAJOR_GE(2020020000)
@@ -397,12 +397,7 @@ TEST_P(DNNTestOpenVINO, models)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
 #endif
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     bool isFP16 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD);
 
diff --git a/modules/dnn/test/test_layers.cpp b/modules/dnn/test/test_layers.cpp
index 8bbccdbc96..3f4a437637 100644
--- a/modules/dnn/test/test_layers.cpp
+++ b/modules/dnn/test/test_layers.cpp
@@ -1265,12 +1265,7 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
     Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
@@ -1310,12 +1305,7 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     int blobSize[] = {2, 6, 75, 113};
     Mat inputs[] = {Mat(4, &blobSize[0], CV_8U), Mat()};
@@ -1348,12 +1338,7 @@ TEST_P(Layer_Test_Convolution_DLDT, multithreading)
     if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         throw SkipTestException("No support for async forward");
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     std::string xmlPath = _tf("layer_convolution.xml");
     std::string binPath = _tf("layer_convolution.bin");
diff --git a/modules/dnn/test/test_misc.cpp b/modules/dnn/test/test_misc.cpp
index 39bb73a918..108131bd9d 100644
--- a/modules/dnn/test/test_misc.cpp
+++ b/modules/dnn/test/test_misc.cpp
@@ -117,12 +117,7 @@ void test_readNet_IE_do_not_call_setInput(Backend backendId)
     const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
     const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     Net net = readNet(model, proto);
     net.setPreferableBackend(backendId);
@@ -462,12 +457,7 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_single)
     const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
     const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     Net netSync = readNet(model, proto);
     netSync.setPreferableBackend(backendId);
@@ -523,12 +513,7 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_all)
     const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
     const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     Net netSync = readNet(model, proto);
     netSync.setPreferableBackend(backendId);
@@ -586,12 +571,7 @@ TEST_P(Async, create_layer_pipeline_set_and_forward_all)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && dtype == CV_8U)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     Net netSync;
     Net netAsync;
@@ -697,12 +677,7 @@ TEST_P(Test_Model_Optimizer, forward_two_nets)
     const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
     const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     Net net0 = readNet(model, proto);
     net0.setPreferableTarget(targetId);
@@ -741,12 +716,7 @@ TEST_P(Test_Model_Optimizer, readFromBuffer)
     const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution.bin");
     const std::string& modelFile = findDataFile("dnn/layers/layer_convolution.xml");
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     Net net1 = readNetFromModelOptimizer(modelFile, weightsFile);
     net1.setPreferableBackend(backendId);
@@ -793,12 +763,7 @@ TEST_P(Test_Model_Optimizer, flexible_inputs)
     const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
     const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
 
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
-    else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
-    else
-        FAIL() << "Unknown backendId";
+    ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
 
     Net net0 = readNet(model, proto);
     net0.setPreferableTarget(targetId);
diff --git a/modules/imgproc/include/opencv2/imgproc.hpp b/modules/imgproc/include/opencv2/imgproc.hpp
index eacec61190..2f5423643b 100644
--- a/modules/imgproc/include/opencv2/imgproc.hpp
+++ b/modules/imgproc/include/opencv2/imgproc.hpp
@@ -3559,10 +3559,11 @@ a mask and then extract the contour, or copy the region to another image, and so
 function unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
 the details below.
 @param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
-taller than image. Since this is both an input and output parameter, you must take responsibility
-of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example,
+taller than image. If an empty Mat is passed it will be created automatically. Since this is both an
+input and output parameter, you must take responsibility of initializing it.
+Flood-filling cannot go across non-zero pixels in the input mask. For example,
 an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
-mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags
+mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags
 as described below. Additionally, the function fills the border of the mask with ones to simplify
 internal processing. It is therefore possible to use the same mask in multiple calls to the function
 to make sure the filled areas do not overlap.
@@ -4832,13 +4833,11 @@ CV_EXPORTS_W double getFontScaleFromHeight(const int fontFace,
                                            const int pixelHeight,
                                            const int thickness = 1);
 
-/** @brief Line iterator
+/** @brief Class for iterating over all pixels on a raster line segment.
 
-The class is used to iterate over all the pixels on the raster line
-segment connecting two specified points.
-
-The class LineIterator is used to get each pixel of a raster line. It
-can be treated as versatile implementation of the Bresenham algorithm
+The class LineIterator is used to get each pixel of a raster line connecting
+two specified points.
+It can be treated as a versatile implementation of the Bresenham algorithm
 where you can stop at each pixel and do some extra processing, for
 example, grab pixel values along the line or draw a line with an effect
 (for example, with XOR operation).
@@ -4867,14 +4866,19 @@ for(int i = 0; i < it2.count; i++, ++it2)
 class CV_EXPORTS LineIterator
 {
 public:
-    /** @brief initializes the iterator
-
-    creates iterators for the line connecting pt1 and pt2
-    the line will be clipped on the image boundaries
-    the line is 8-connected or 4-connected
-    If leftToRight=true, then the iteration is always done
-    from the left-most point to the right most,
-    not to depend on the ordering of pt1 and pt2 parameters;
+    /** @brief Initializes iterator object for the given line and image.
+
+    The returned iterator can be used to traverse all pixels on a line that
+    connects the given two points.
+    The line will be clipped on the image boundaries.
+
+    @param img Underlying image.
+    @param pt1 First endpoint of the line.
+    @param pt2 The other endpoint of the line.
+    @param connectivity Pixel connectivity of the iterator. Valid values are 4 (iterator can move
+    up, down, left and right) and 8 (iterator can also move diagonally).
+    @param leftToRight If true, the line is traversed from the leftmost endpoint to the rightmost
+    endpoint. Otherwise, the line is traversed from \p pt1 to \p pt2.
     */
     LineIterator( const Mat& img, Point pt1, Point pt2,
                   int connectivity = 8, bool leftToRight = false )
@@ -4907,16 +4911,23 @@ public:
     }
     void init(const Mat* img, Rect boundingAreaRect, Point pt1, Point pt2, int connectivity, bool leftToRight);
 
-    /** @brief returns pointer to the current pixel
+    /** @brief Returns pointer to the current pixel.
     */
     uchar* operator *();
-    /** @brief prefix increment operator (++it). shifts iterator to the next pixel
+
+    /** @brief Moves iterator to the next pixel on the line.
+
+    This is the prefix version (++it).
     */
     LineIterator& operator ++();
-    /** @brief postfix increment operator (it++). shifts iterator to the next pixel
+
+    /** @brief Moves iterator to the next pixel on the line.
+
+    This is the postfix version (it++).
     */
     LineIterator operator ++(int);
-    /** @brief returns coordinates of the current pixel
+
+    /** @brief Returns coordinates of the current pixel.
     */
     Point pos() const;
 
diff --git a/modules/imgproc/src/floodfill.cpp b/modules/imgproc/src/floodfill.cpp
index 2816795bc6..8595011d48 100644
--- a/modules/imgproc/src/floodfill.cpp
+++ b/modules/imgproc/src/floodfill.cpp
@@ -477,11 +477,10 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
     nv_buf._[0] = nv_buf._[1] = nv_buf._[2] = nv_buf._[3] = 0;
 
     struct { Vec3b b; Vec3i i; Vec3f f; } ld_buf, ud_buf;
+
     Mat img = _image.getMat(), mask;
-    if( !_mask.empty() )
-        mask = _mask.getMat();
-    Size size = img.size();
 
+    Size size = img.size();
     int type = img.type();
     int depth = img.depth();
     int cn = img.channels();
@@ -495,6 +494,20 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
     if( connectivity != 0 && connectivity != 4 && connectivity != 8 )
         CV_Error( CV_StsBadFlag, "Connectivity must be 4, 0(=4) or 8" );
 
+    if( _mask.empty() )
+    {
+        _mask.create( size.height + 2, size.width + 2, CV_8UC1 );
+        _mask.setTo(0);
+    }
+
+    mask = _mask.getMat();
+    CV_CheckTypeEQ( mask.type(), CV_8U, "" );
+    CV_CheckEQ( mask.rows, size.height + 2, "" );
+    CV_CheckEQ( mask.cols, size.width + 2, "" );
+
+    Mat mask_inner = mask( Rect(1, 1, mask.cols - 2, mask.rows - 2) );
+    copyMakeBorder( mask_inner, mask, 1, 1, 1, 1, BORDER_ISOLATED | BORDER_CONSTANT, Scalar(1) );
+
     bool is_simple = mask.empty() && (flags & FLOODFILL_MASK_ONLY) == 0;
 
     for( i = 0; i < cn; i++ )
@@ -544,26 +557,6 @@ int cv::floodFill( InputOutputArray _image, InputOutputArray _mask,
         }
     }
 
-    if( mask.empty() )
-    {
-        Mat tempMask( size.height + 2, size.width + 2, CV_8UC1 );
-        tempMask.setTo(Scalar::all(0));
-        mask = tempMask;
-    }
-    else
-    {
-        CV_Assert( mask.rows == size.height+2 && mask.cols == size.width+2 );
-        CV_Assert( mask.type() == CV_8U );
-    }
-
-    memset( mask.ptr(), 1, mask.cols );
-    memset( mask.ptr(mask.rows-1), 1, mask.cols );
-
-    for( i = 1; i <= size.height; i++ )
-    {
-        mask.at<uchar>(i, 0) = mask.at<uchar>(i, mask.cols-1) = (uchar)1;
-    }
-
     if( depth == CV_8U )
         for( i = 0; i < cn; i++ )
         {
@@ -632,7 +625,8 @@ int cv::floodFill( InputOutputArray _image, Point seedPoint,
 {
     CV_INSTRUMENT_REGION();
 
-    return floodFill(_image, Mat(), seedPoint, newVal, rect, loDiff, upDiff, flags);
+    Mat mask;
+    return floodFill(_image, mask, seedPoint, newVal, rect, loDiff, upDiff, flags);
 }
 
 
diff --git a/modules/imgproc/test/test_floodfill.cpp b/modules/imgproc/test/test_floodfill.cpp
index b880c4ee37..934e421fba 100644
--- a/modules/imgproc/test/test_floodfill.cpp
+++ b/modules/imgproc/test/test_floodfill.cpp
@@ -531,11 +531,11 @@ TEST(Imgproc_FloodFill, maskValue)
 {
     const int n = 50;
     Mat img = Mat::zeros(n, n, CV_8U);
-    Mat mask = Mat::zeros(n + 2, n + 2, CV_8U);
+    Mat mask;
 
     circle(img, Point(n/2, n/2), 20, Scalar(100), 4);
 
-    int flags = 4 + CV_FLOODFILL_MASK_ONLY;
+    int flags = 4 + FLOODFILL_MASK_ONLY;
     floodFill(img, mask, Point(n/2 + 13, n/2), Scalar(100), NULL, Scalar(),  Scalar(), flags);
 
     ASSERT_EQ(1, cvtest::norm(mask.rowRange(1, n-1).colRange(1, n-1), NORM_INF));
diff --git a/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig-version.cmake b/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig-version.cmake
new file mode 100644
index 0000000000..bc449d05cd
--- /dev/null
+++ b/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig-version.cmake
@@ -0,0 +1,29 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(PACKAGE_VERSION_MAJOR 2021)
+set(PACKAGE_VERSION_MINOR 4)
+set(PACKAGE_VERSION_PATCH 2)
+set(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
+
+set(PACKAGE_VERSION_EXACT False)
+set(PACKAGE_VERSION_COMPATIBLE False)
+
+# Compatibility with old versioning for 2.x
+if(PACKAGE_FIND_VERSION_MAJOR VERSION_EQUAL 2)
+    set(PACKAGE_VERSION_COMPATIBLE True)
+    if(${CMAKE_FIND_PACKAGE_NAME}_FIND_REQUIRED)
+        message(WARNING "Inference Engine versioning has changed. Use ${PACKAGE_VERSION} instead of ${PACKAGE_FIND_VERSION}")
+    endif()
+endif()
+
+if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION)
+    set(PACKAGE_VERSION_EXACT True)
+    set(PACKAGE_VERSION_COMPATIBLE True)
+endif()
+
+if(PACKAGE_FIND_VERSION_MAJOR EQUAL PACKAGE_VERSION_MAJOR AND
+   PACKAGE_FIND_VERSION VERSION_LESS PACKAGE_VERSION)
+    set(PACKAGE_VERSION_COMPATIBLE True)
+endif()
diff --git a/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig.cmake b/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig.cmake
new file mode 100644
index 0000000000..d9c9a12de6
--- /dev/null
+++ b/platforms/winpack_dldt/2021.4.2/cmake/InferenceEngineConfig.cmake
@@ -0,0 +1,31 @@
+# Inference Engine CMake config for OpenCV windows package
+
+get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+
+set(InferenceEngine_LIBRARIES IE::inference_engine)
+add_library(IE::inference_engine SHARED IMPORTED)
+
+set_target_properties(IE::inference_engine PROPERTIES
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/deployment_tools/inference_engine/include"
+)
+
+# Import target "IE::inference_engine" for configuration "Debug"
+set_property(TARGET IE::inference_engine APPEND PROPERTY IMPORTED_CONFIGURATIONS DEBUG)
+set_target_properties(IE::inference_engine PROPERTIES
+  IMPORTED_IMPLIB_DEBUG "${_IMPORT_PREFIX}/deployment_tools/inference_engine/lib/intel64/inference_engined.lib"
+  IMPORTED_LINK_DEPENDENT_LIBRARIES_DEBUG ""
+  IMPORTED_LOCATION_DEBUG "${_IMPORT_PREFIX}/bin/inference_engined.dll"
+  )
+
+# Import target "IE::inference_engine" for configuration "Release"
+set_property(TARGET IE::inference_engine APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(IE::inference_engine PROPERTIES
+  IMPORTED_IMPLIB_RELEASE "${_IMPORT_PREFIX}/deployment_tools/inference_engine/lib/intel64/inference_engine.lib"
+  IMPORTED_LINK_DEPENDENT_LIBRARIES_RELEASE ""
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/bin/inference_engine.dll"
+  )
+
+set(InferenceEngine_FOUND ON)
diff --git a/platforms/winpack_dldt/2021.4.2/sysroot.config.py b/platforms/winpack_dldt/2021.4.2/sysroot.config.py
index fa4281107d..f11e99f843 100644
--- a/platforms/winpack_dldt/2021.4.2/sysroot.config.py
+++ b/platforms/winpack_dldt/2021.4.2/sysroot.config.py
@@ -1,3 +1,5 @@
+copytree(self.cpath / 'cmake', self.sysrootdir / 'deployment_tools' / 'inference_engine' / 'cmake')
+
 sysroot_bin_dir = prepare_dir(self.sysrootdir / 'bin')
 copytree(self.build_dir / 'install', self.sysrootdir / 'ngraph')
 #rm_one(self.sysrootdir / 'ngraph' / 'lib' / 'ngraph.dll')
diff --git a/platforms/winpack_dldt/build_package.py b/platforms/winpack_dldt/build_package.py
index 88154bafb5..277a13c232 100644
--- a/platforms/winpack_dldt/build_package.py
+++ b/platforms/winpack_dldt/build_package.py
@@ -388,10 +388,9 @@ class Builder:
         if self.config.dldt_release:
             cmake_vars['INF_ENGINE_RELEASE'] = str(self.config.dldt_release)
 
-        cmake_vars['INF_ENGINE_LIB_DIRS:PATH'] = str(builderDLDT.sysrootdir / 'deployment_tools/inference_engine/lib/intel64')
-        assert os.path.exists(cmake_vars['INF_ENGINE_LIB_DIRS:PATH']), cmake_vars['INF_ENGINE_LIB_DIRS:PATH']
-        cmake_vars['INF_ENGINE_INCLUDE_DIRS:PATH'] = str(builderDLDT.sysrootdir / 'deployment_tools/inference_engine/include')
-        assert os.path.exists(cmake_vars['INF_ENGINE_INCLUDE_DIRS:PATH']), cmake_vars['INF_ENGINE_INCLUDE_DIRS:PATH']
+        InferenceEngine_DIR = str(builderDLDT.sysrootdir / 'deployment_tools' / 'inference_engine' / 'cmake')
+        assert os.path.exists(InferenceEngine_DIR), InferenceEngine_DIR
+        cmake_vars['InferenceEngine_DIR:PATH'] = InferenceEngine_DIR
 
         ngraph_DIR = str(builderDLDT.sysrootdir / 'ngraph/cmake')
         if not os.path.exists(ngraph_DIR):
diff --git a/samples/dnn/text_detection.py b/samples/dnn/text_detection.py
index 6fb1e90901..db0ea197bd 100644
--- a/samples/dnn/text_detection.py
+++ b/samples/dnn/text_detection.py
@@ -195,7 +195,7 @@ def main():
         indices = cv.dnn.NMSBoxesRotated(boxes, confidences, confThreshold, nmsThreshold)
         for i in indices:
             # get 4 corners of the rotated rect
-            vertices = cv.boxPoints(boxes[i[0]])
+            vertices = cv.boxPoints(boxes[i])
             # scale the bounding box coordinates based on the respective ratios
             for j in range(4):
                 vertices[j][0] *= rW