diff --git a/doc/Doxyfile.in b/doc/Doxyfile.in
index ce7a91dbf1..ec7d7cd3b3 100644
--- a/doc/Doxyfile.in
+++ b/doc/Doxyfile.in
@@ -246,7 +246,7 @@ PREDEFINED             = __cplusplus=1 \
                          CV_WRAP_PHANTOM(x)= \
                          CV_WRAP_DEFAULT(x)= \
                          CV_CDECL= \
-                         CV_Func = \
+                         CV_Func= \
                          CV_DO_PRAGMA(x)= \
                          CV_SUPPRESS_DEPRECATED_START= \
                          CV_SUPPRESS_DEPRECATED_END= \
diff --git a/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown b/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown
index 17844b7e80..9851599455 100644
--- a/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown
+++ b/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown
@@ -80,7 +80,7 @@ Probabilistic Hough Transform
 In the hough transform, you can see that even for a line with two arguments, it takes a lot of
 computation. Probabilistic Hough Transform is an optimization of the Hough Transform we saw. It doesn't
 take all the points into consideration. Instead, it takes only a random subset of points which is
-sufficient for line detection. Just we have to decrease the threshold. See image below which compares
+sufficient for line detection. We just have to decrease the threshold. See image below which compares
 Hough Transform and Probabilistic Hough Transform in Hough space. (Image Courtesy :
 [Franck Bettinger's home page](http://phdfb1.free.fr/robot/mscthesis/node14.html) )
 
diff --git a/modules/core/include/opencv2/core/utility.hpp b/modules/core/include/opencv2/core/utility.hpp
index 36aebf5996..3d2a03593f 100644
--- a/modules/core/include/opencv2/core/utility.hpp
+++ b/modules/core/include/opencv2/core/utility.hpp
@@ -622,6 +622,7 @@ void Mat::forEach_impl(const Functor& operation) {
         //  or (_Tp&, void*)        <- in case you don't need current idx.
     }
 
+    CV_Assert(!empty());
     CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX);
     const int LINES = static_cast<int>(this->total() / this->size[this->dims - 1]);
 
diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp
index 53b316940c..f7387a0c70 100644
--- a/modules/dnn/src/dnn.cpp
+++ b/modules/dnn/src/dnn.cpp
@@ -2228,7 +2228,11 @@ struct Net::Impl
 
                     auto ieInpNode = inputNodes[i].dynamicCast<InfEngineNgraphNode>();
                     CV_Assert(oid < ieInpNode->node->get_output_size());
+#if INF_ENGINE_VER_MAJOR_GT(2020020000)
+                    inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid)));
+#else
                     inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid, false)));
+#endif
                 }
 
                 if (layer->supportBackend(preferableBackend))
diff --git a/modules/imgproc/test/ocl/test_houghlines.cpp b/modules/imgproc/test/ocl/test_houghlines.cpp
index e6cb439403..4e7b8917ac 100644
--- a/modules/imgproc/test/ocl/test_houghlines.cpp
+++ b/modules/imgproc/test/ocl/test_houghlines.cpp
@@ -173,7 +173,7 @@ OCL_TEST_P(HoughLinesP, RealImage)
 
 OCL_INSTANTIATE_TEST_CASE_P(Imgproc, HoughLines, Combine(Values(1, 0.5),                        // rhoStep
                                                          Values(CV_PI / 180.0, CV_PI / 360.0),  // thetaStep
-                                                         Values(80, 150)));                     // threshold
+                                                         Values(85, 150)));                     // threshold
 
 OCL_INSTANTIATE_TEST_CASE_P(Imgproc, HoughLinesP, Combine(Values(100, 150),                     // threshold
                                                           Values(50, 100),                      // minLineLength