diff --git a/modules/dnn/samples/ssd_ilsvrc15_300x300.prototxt b/modules/dnn/samples/ssd_ilsvrc15_300x300.prototxt
index 120e78e91..15a0c2aba 100644
--- a/modules/dnn/samples/ssd_ilsvrc15_300x300.prototxt
+++ b/modules/dnn/samples/ssd_ilsvrc15_300x300.prototxt
@@ -500,7 +500,7 @@ layer {
       type: "constant"
       value: 0
     }
-    # dilation: 6
+    dilation: 6
   }
 }
 layer {
@@ -750,7 +750,7 @@ layer {
 }
 layer {
   name: "conv4_3_norm"
-  type: "Normalize"
+  type: "NormalizeBBox"
   bottom: "conv4_3"
   top: "conv4_3_norm"
   normalize_bbox_param {
diff --git a/modules/dnn/samples/ssd_object_detection.cpp b/modules/dnn/samples/ssd_object_detection.cpp
index 77567f155..0d12ac53c 100644
--- a/modules/dnn/samples/ssd_object_detection.cpp
+++ b/modules/dnn/samples/ssd_object_detection.cpp
@@ -9,48 +9,46 @@ using namespace cv::dnn;
 #include <cstdlib>
 using namespace std;
 
-static const string fcnType = "fcn8s";
 
-
-static void colorizeSegmentation(dnn::Blob& score,
-                                 const vector<cv::Vec3b>& colors,
-                                 cv::Mat& segm)
-{
-    const int rows = score.rows();
-    const int cols = score.cols();
-    const int chns = score.channels();
-
-    cv::Mat maxCl(rows, cols, CV_8UC1);
-    cv::Mat maxVal(rows, cols, CV_32FC1);
-    for (int ch = 0; ch < chns; ch++)
-    {
-        for (int row = 0; row < rows; row++)
-        {
-            const float* ptrScore = score.ptrf(0, ch, row);
-            uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
-            float* ptrMaxVal = maxVal.ptr<float>(row);
-            for (int col = 0; col < cols; col++)
-            {
-                if (ptrScore[col] > ptrMaxVal[col])
-                {
-                    ptrMaxVal[col] = ptrScore[col];
-                    ptrMaxCl[col] = ch;
-                }
-            }
-        }
-    }
-
-    segm.create(rows, cols, CV_8UC3);
-    for (int row = 0; row < rows; row++)
-    {
-        const uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
-        cv::Vec3b* ptrSegm = segm.ptr<cv::Vec3b>(row);
-        for (int col = 0; col < cols; col++)
-        {
-            ptrSegm[col] = colors[ptrMaxCl[col]];
-        }
-    }
-}
+//static void colorizeSegmentation(dnn::Blob& score,
+//                                 const vector<cv::Vec3b>& colors,
+//                                 cv::Mat& segm)
+//{
+//    const int rows = score.rows();
+//    const int cols = score.cols();
+//    const int chns = score.channels();
+
+//    cv::Mat maxCl(rows, cols, CV_8UC1);
+//    cv::Mat maxVal(rows, cols, CV_32FC1);
+//    for (int ch = 0; ch < chns; ch++)
+//    {
+//        for (int row = 0; row < rows; row++)
+//        {
+//            const float* ptrScore = score.ptrf(0, ch, row);
+//            uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
+//            float* ptrMaxVal = maxVal.ptr<float>(row);
+//            for (int col = 0; col < cols; col++)
+//            {
+//                if (ptrScore[col] > ptrMaxVal[col])
+//                {
+//                    ptrMaxVal[col] = ptrScore[col];
+//                    ptrMaxCl[col] = ch;
+//                }
+//            }
+//        }
+//    }
+
+//    segm.create(rows, cols, CV_8UC3);
+//    for (int row = 0; row < rows; row++)
+//    {
+//        const uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
+//        cv::Vec3b* ptrSegm = segm.ptr<cv::Vec3b>(row);
+//        for (int col = 0; col < cols; col++)
+//        {
+//            ptrSegm[col] = colors[ptrMaxCl[col]];
+//        }
+//    }
+//}
 
 const char* about = "This sample uses Single-Shot Detector to detect objects "
                     "from camera\n"; // TODO: link
@@ -111,6 +109,7 @@ int main(int argc, char** argv)
         return 0;
     }
 
+    size_t i = 0;
     for (;; )
     {
         Mat frame;
@@ -118,12 +117,15 @@ int main(int argc, char** argv)
         if (frame.empty())
             break;
 
-
         //! [Prepare blob]
         resize(frame, frame, Size(300, 300));       //SSD accepts 300x300 RGB-images
         dnn::Blob inputBlob = dnn::Blob(frame);       //Convert Mat to dnn::Blob image
         //! [Prepare blob]
 
+        std::ostringstream stream;
+        stream << "folder/" << i << ".jpg";
+        imwrite(stream.str(), frame);
+
         //! [Set input blob]
         net.setBlob(".data", inputBlob);            //set the network input
         //! [Set input blob]
@@ -132,20 +134,20 @@ int main(int argc, char** argv)
         net.forward();                              //compute output
         //! [Make forward pass]
 
-        //! [Gather output]
-        dnn::Blob detection = net.getBlob("detection_out");
+//        //! [Gather output]
+//        dnn::Blob detection = net.getBlob("detection_out");
 
-        //    cv::Mat colorize;
-        //    colorizeSegmentation(score, colors, colorize);
-        //    cv::Mat show;
-        //    cv::addWeighted(img, 0.4, colorize, 0.6, 0.0, show);
-        //    cv::imshow("show", show);
-        //    cv::waitKey(0);
-        //    return 0;
+//        //    cv::Mat colorize;
+//        //    colorizeSegmentation(score, colors, colorize);
+//        //    cv::Mat show;
+//        //    cv::addWeighted(img, 0.4, colorize, 0.6, 0.0, show);
+//        //    cv::imshow("show", show);
+//        //    cv::waitKey(0);
+//        //    return 0;
 
-        imshow("frame", frame);
-        if (waitKey(1) == 27)
-            break;  // stop capturing by pressing ESC
+//        imshow("frame", frame);
+//        if (waitKey(1) == 27)
+//            break;  // stop capturing by pressing ESC
     }
 
     camera.release();
diff --git a/modules/dnn/src/caffe/caffe.proto b/modules/dnn/src/caffe/caffe.proto
index 6a92803a1..a7bca16ad 100644
--- a/modules/dnn/src/caffe/caffe.proto
+++ b/modules/dnn/src/caffe/caffe.proto
@@ -404,7 +404,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 138 (last added: detection_output_param)
+// LayerParameter next available layer-specific ID: 142 (last added: detection_output_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
diff --git a/modules/dnn/src/init.cpp b/modules/dnn/src/init.cpp
index 03546bb16..7e285a6fd 100644
--- a/modules/dnn/src/init.cpp
+++ b/modules/dnn/src/init.cpp
@@ -55,6 +55,11 @@
 #include "layers/split_layer.hpp"
 #include "layers/crop_layer.hpp"
 #include "layers/eltwise_layer.hpp"
+#include "layers/flatten_layer.hpp"
+#include "layers/permute_layer.hpp"
+#include "layers/prior_box_layer.hpp"
+#include "layers/detection_output_layer.hpp"
+#include "layers/normalize_bbox_layer.hpp"
 
 namespace cv
 {
@@ -82,7 +87,7 @@ void initModule()
     REG_RUNTIME_LAYER_CLASS(Softmax, SoftMaxLayer)
     REG_RUNTIME_LAYER_CLASS(Split, SplitLayer)
     REG_RUNTIME_LAYER_CLASS(Reshape, ReshapeLayer)
-    REG_STATIC_LAYER_FUNC(Flatten, createFlattenLayer)
+//    REG_STATIC_LAYER_FUNC(Flatten, createFlattenLayer)
     REG_RUNTIME_LAYER_CLASS(Pooling, PoolingLayer)
     REG_RUNTIME_LAYER_CLASS(MVN, MVNLayer)
     REG_RUNTIME_LAYER_CLASS(LRN, LRNLayer)
@@ -103,6 +108,12 @@ void initModule()
     REG_RUNTIME_LAYER_CLASS(Crop, CropLayer)
     REG_RUNTIME_LAYER_CLASS(Eltwise, EltwiseLayer)
 
+    REG_RUNTIME_LAYER_CLASS(Permute, PermuteLayer)
+    REG_RUNTIME_LAYER_CLASS(Flatten, FlattenLayer)
+    REG_RUNTIME_LAYER_CLASS(PriorBox, PriorBoxLayer)
+    REG_RUNTIME_LAYER_CLASS(DetectionOutput, DetectionOutputLayer)
+    REG_RUNTIME_LAYER_CLASS(NormalizeBBox, NormalizeBBoxLayer)
+
     init.status = true;
 }
 
diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp
index 03d76dc7b..4934fba37 100644
--- a/modules/dnn/src/layers/convolution_layer.cpp
+++ b/modules/dnn/src/layers/convolution_layer.cpp
@@ -190,8 +190,9 @@ namespace dnn
         inpW = inpBlob.cols();
         inpCn = inpBlob.channels();
 
-        outH = (inpH + 2 * padH - kerH) / strideH + 1;
-        outW = (inpW + 2 * padW - kerW) / strideW + 1;
+        outH = (inpH + 2 * padH - (dilationH * (kerH - 1) + 1)) / strideH + 1;
+        outW = (inpW + 2 * padW - (dilationW * (kerW - 1) + 1)) / strideW + 1;
+
         outCn = numOutput;
 
         topH = outH; topW = outW; topCn = outCn;
diff --git a/modules/dnn/src/layers/detection_output_layer.cpp b/modules/dnn/src/layers/detection_output_layer.cpp
index 3a18ed0b9..73ef9558c 100644
--- a/modules/dnn/src/layers/detection_output_layer.cpp
+++ b/modules/dnn/src/layers/detection_output_layer.cpp
@@ -79,65 +79,78 @@ bool SortScorePairDescend(const std::pair<float, T>& pair1,
 
 const std::string DetectionOutputLayer::_layerName = std::string("DetectionOutput");
 
-DictValue DetectionOutputLayer::getParameterDict(const LayerParams &params,
-                                                 const std::string &parameterName)
+bool DetectionOutputLayer::getParameterDict(const LayerParams &params,
+                                    const std::string &parameterName,
+                                    DictValue& result)
 {
     if (!params.has(parameterName))
     {
-        std::string message = _layerName;
-        message += " layer parameter does not contain ";
-        message += parameterName;
-        message += " index.";
-        CV_Error(Error::StsBadArg, message);
+        return false;
     }
 
-    DictValue parameter = params.get(parameterName);
-    if(parameter.size() != 1)
-    {
-        std::string message = parameterName;
-        message += " field in ";
-        message += _layerName;
-        message += " layer parameter is required";
-        CV_Error(Error::StsBadArg, message);
-    }
-
-    return parameter;
+    result = params.get(parameterName);
+    return true;
 }
 
 template<typename T>
 T DetectionOutputLayer::getParameter(const LayerParams &params,
-                                     const std::string &parameterName,
-                                     const size_t &idx)
+                             const std::string &parameterName,
+                             const size_t &idx,
+                             const bool required,
+                             const T& defaultValue)
+{
+    DictValue dictValue;
+    bool success = getParameterDict(params, parameterName, dictValue);
+    if(!success)
+    {
+        if(required)
+        {
+            std::string message = _layerName;
+            message += " layer parameter does not contain ";
+            message += parameterName;
+            message += " parameter.";
+            CV_Error(Error::StsBadArg, message);
+        }
+        else
+        {
+            return defaultValue;
+        }
+    }
+    return dictValue.get<T>(idx);
+}
+
+void DetectionOutputLayer::getCodeType(LayerParams &params)
 {
-    return getParameterDict(params, parameterName).get<T>(idx);
+    String codeTypeString = params.get<String>("code_type").toLowerCase();
+    if (codeTypeString == "corner")
+        _codeType = caffe::PriorBoxParameter_CodeType_CORNER;
+    else if (codeTypeString == "center_size")
+        _codeType = caffe::PriorBoxParameter_CodeType_CENTER_SIZE;
+    else
+        _codeType = caffe::PriorBoxParameter_CodeType_CORNER;
 }
 
 DetectionOutputLayer::DetectionOutputLayer(LayerParams &params) : Layer(params)
 {
-    _numClasses = getParameter<int>(params, "num_classes");
-    _shareLocation = getParameter<int>(params, "share_location");
+    _numClasses = getParameter<unsigned>(params, "num_classes");
+    _shareLocation = getParameter<bool>(params, "share_location");
     _numLocClasses = _shareLocation ? 1 : _numClasses;
     _backgroundLabelId = getParameter<int>(params, "background_label_id");
-    _codeType = static_cast<CodeType>(getParameter<int>(params, "code_type"));
-    _varianceEncodedInTarget = getParameter<int>(params, "variance_encoded_in_target");
+    _varianceEncodedInTarget = getParameter<bool>(params, "variance_encoded_in_target", 0, false, false);
     _keepTopK = getParameter<int>(params, "keep_top_k");
-    _confidenceThreshold = params.has("confidence_threshold") ?
-                           getParameter<int>(params, "confidence_threshold") : -FLT_MAX;
+    _confidenceThreshold = getParameter<float>(params, "confidence_threshold", 0, false, -FLT_MAX);
+    _topK = getParameter<int>(params, "top_k", 0, false, -1);
+
+    getCodeType(params);
 
     // Parameters used in nms.
     _nmsThreshold = getParameter<float>(params, "nms_threshold");
     CV_Assert(_nmsThreshold > 0.);
-
-    _topK = -1;
-    if (params.has("top_k"))
-    {
-        _topK = getParameter<int>(params, "top_k");
-    }
 }
 
 void DetectionOutputLayer::checkInputs(const std::vector<Blob*> &inputs)
 {
-    for (size_t i = 0; i < inputs.size(); i++)
+    for (size_t i = 1; i < inputs.size(); i++)
     {
         for (size_t j = 0; j < _numAxes; j++)
         {
diff --git a/modules/dnn/src/layers/detection_output_layer.hpp b/modules/dnn/src/layers/detection_output_layer.hpp
index ddcc03d87..0b28d69a8 100644
--- a/modules/dnn/src/layers/detection_output_layer.hpp
+++ b/modules/dnn/src/layers/detection_output_layer.hpp
@@ -51,7 +51,7 @@ namespace dnn
 {
 class DetectionOutputLayer : public Layer
 {
-    int _numClasses;
+    unsigned _numClasses;
     bool _shareLocation;
     int _numLocClasses;
 
@@ -79,13 +79,18 @@ public:
     void forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
 
     void checkInputs(const std::vector<Blob*> &inputs);
+    void getCodeType(LayerParams &params);
 
     template<typename T>
-    T getParameter(const LayerParams &params, const std::string &parameterName,
-                   const size_t &idx = 0);
-
-    DictValue getParameterDict(const LayerParams &params,
-                               const std::string &parameterName);
+    T getParameter(const LayerParams &params,
+                   const std::string &parameterName,
+                   const size_t &idx = 0,
+                   const bool required = true,
+                   const T& defaultValue = T());
+
+    bool getParameterDict(const LayerParams &params,
+                          const std::string &parameterName,
+                          DictValue& result);
 
     typedef std::map<int, std::vector<caffe::NormalizedBBox> > LabelBBox;
 
diff --git a/modules/dnn/src/layers/flatten_layer.cpp b/modules/dnn/src/layers/flatten_layer.cpp
index c84912f35..4dd256a5f 100644
--- a/modules/dnn/src/layers/flatten_layer.cpp
+++ b/modules/dnn/src/layers/flatten_layer.cpp
@@ -52,54 +52,56 @@ namespace dnn
 
 const std::string FlattenLayer::_layerName = std::string("Flatten");
 
-DictValue FlattenLayer::getParameterDict(const LayerParams &params,
-                                          const std::string &parameterName)
+bool FlattenLayer::getParameterDict(const LayerParams &params,
+                                    const std::string &parameterName,
+                                    DictValue& result)
 {
     if (!params.has(parameterName))
     {
-        std::string message = _layerName;
-        message += " layer parameter does not contain ";
-        message += parameterName;
-        message += " index.";
-        CV_Error(Error::StsBadArg, message);
+        return false;
     }
 
-    DictValue parameter = params.get(parameterName);
-    if(parameter.size() != 1)
-    {
-        std::string message = parameterName;
-        message += " field in ";
-        message += _layerName;
-        message += " layer parameter is required";
-        CV_Error(Error::StsBadArg, message);
-    }
-
-    return parameter;
+    result = params.get(parameterName);
+    return true;
 }
 
 template<typename T>
 T FlattenLayer::getParameter(const LayerParams &params,
-                              const std::string &parameterName,
-                              const size_t &idx)
+                             const std::string &parameterName,
+                             const size_t &idx,
+                             const bool required,
+                             const T& defaultValue)
 {
-    return getParameterDict(params, parameterName).get<T>(idx);
+    DictValue dictValue;
+    bool success = getParameterDict(params, parameterName, dictValue);
+    if(!success)
+    {
+        if(required)
+        {
+            std::string message = _layerName;
+            message += " layer parameter does not contain ";
+            message += parameterName;
+            message += " parameter.";
+            CV_Error(Error::StsBadArg, message);
+        }
+        else
+        {
+            return defaultValue;
+        }
+    }
+    return dictValue.get<T>(idx);
 }
 
 FlattenLayer::FlattenLayer(LayerParams &params) : Layer(params)
 {
-    _startAxis = getParameter<int>(params, "start_axis");
-    _endAxis = getParameter<int>(params, "end_axis");
-
-    if(_endAxis <= 0)
-    {
-        _endAxis += _numAxes;
-    }
+    _startAxis = getParameter<int>(params, "axis");
+    _endAxis = getParameter<int>(params, "end_axis", 0, false, -1);
 }
 
 void FlattenLayer::checkInputs(const std::vector<Blob*> &inputs)
 {
     CV_Assert(inputs.size() > 0);
-    for (size_t i = 0; i < inputs.size(); i++)
+    for (size_t i = 1; i < inputs.size(); i++)
     {
         for (size_t j = 0; j < _numAxes; j++)
         {
@@ -112,29 +114,31 @@ void FlattenLayer::allocate(const std::vector<Blob*> &inputs, std::vector<Blob>
 {
     checkInputs(inputs);
 
+    _numAxes = inputs[0]->shape().dims();
+    if(_endAxis <= 0)
+    {
+        _endAxis += _numAxes;
+    }
+    CV_Assert(_startAxis >= 0);
+    CV_Assert(_endAxis >= _startAxis && _endAxis < (int)_numAxes);
+
     size_t flattenedDimensionSize = 1;
     for (int i = _startAxis; i <= _endAxis; i++)
     {
         flattenedDimensionSize *= inputs[0]->shape()[i];
     }
 
-    BlobShape outputShape;
-    size_t interval = _endAxis - _startAxis;
-
-    // imitate flexible number of axes: make first dimension sizes = 1
-    for (size_t i = 0; i < interval; i++)
-    {
-        outputShape[i] = 1;
-    }
-    for (int i = interval; i < _endAxis; i++)
+    std::vector<int> outputShape;
+    for (int i = 0; i < _startAxis; i++)
     {
-        outputShape[i] = inputs[0]->shape()[i - interval];
+        outputShape.push_back(inputs[0]->shape()[i]);
     }
-    outputShape[_endAxis] = flattenedDimensionSize;
+    outputShape.push_back(flattenedDimensionSize);
     for (size_t i = _endAxis + 1; i < _numAxes; i++)
     {
-        outputShape[i] = inputs[0]->shape()[i];
+        outputShape.push_back(inputs[0]->shape()[i]);
     }
+    CV_Assert(outputShape.size() <= 4);
 
     for (size_t i = 0; i < inputs.size(); i++)
     {
diff --git a/modules/dnn/src/layers/flatten_layer.hpp b/modules/dnn/src/layers/flatten_layer.hpp
index cb7e35c8e..f940f3989 100644
--- a/modules/dnn/src/layers/flatten_layer.hpp
+++ b/modules/dnn/src/layers/flatten_layer.hpp
@@ -49,10 +49,10 @@ namespace dnn
 {
 class FlattenLayer : public Layer
 {
-    size_t _startAxis;
+    int _startAxis;
     int _endAxis;
 
-    static const size_t _numAxes = 4;
+    size_t _numAxes;
     static const std::string _layerName;
 
 public:
@@ -63,11 +63,14 @@ public:
     void checkInputs(const std::vector<Blob*> &inputs);
 
     template<typename T>
-    T getParameter(const LayerParams &params, const std::string &parameterName,
-                   const size_t &idx = 0);
+    T getParameter(const LayerParams &params,
+                   const std::string &parameterName,
+                   const size_t &idx = 0,
+                   const bool required = true,
+                   const T& defaultValue = T());
 
-    DictValue getParameterDict(const LayerParams &params,
-                               const std::string &parameterName);
+    bool getParameterDict(const LayerParams &params,
+                          const std::string &parameterName, DictValue &result);
 };
 }
 }
diff --git a/modules/dnn/src/layers/layers_common.cpp b/modules/dnn/src/layers/layers_common.cpp
index 24cf842ab..d811a208a 100644
--- a/modules/dnn/src/layers/layers_common.cpp
+++ b/modules/dnn/src/layers/layers_common.cpp
@@ -72,20 +72,21 @@ bool getParameter(LayerParams &params, const std::string& nameBase, const std::s
     else
     {
         if (params.has(nameAll_))
+        {
+            parameterH = parameterW = params.get<int>(nameAll_);
+            return true;
+        }
+        else
         {
             if(hasDefault)
             {
-                parameterH = parameterW = params.get<int>(nameAll_, defaultValue);
+                parameterH = parameterW = defaultValue;
+                return true;
             }
             else
             {
-                parameterH = parameterW = params.get<int>(nameAll_);
+                return false;
             }
-            return true;
-        }
-        else
-        {
-            return false;
         }
     }
 }
diff --git a/modules/dnn/src/layers/normalize_bbox_layer.cpp b/modules/dnn/src/layers/normalize_bbox_layer.cpp
index 108783c03..13a9cb2a3 100644
--- a/modules/dnn/src/layers/normalize_bbox_layer.cpp
+++ b/modules/dnn/src/layers/normalize_bbox_layer.cpp
@@ -54,43 +54,49 @@ namespace dnn
 
 const std::string NormalizeBBoxLayer::_layerName = std::string("NormalizeBBox");
 
-DictValue NormalizeBBoxLayer::getParameterDict(const LayerParams &params,
-                                               const std::string &parameterName)
+bool NormalizeBBoxLayer::getParameterDict(const LayerParams &params,
+                                          const std::string &parameterName,
+                                          DictValue& result)
 {
     if (!params.has(parameterName))
     {
-        std::string message = _layerName;
-        message += " layer parameter does not contain ";
-        message += parameterName;
-        message += " index.";
-        CV_Error(Error::StsBadArg, message);
+        return false;
     }
 
-    DictValue parameter = params.get(parameterName);
-    if(parameter.size() != 1)
-    {
-        std::string message = parameterName;
-        message += " field in ";
-        message += _layerName;
-        message += " layer parameter is required";
-        CV_Error(Error::StsBadArg, message);
-    }
-
-    return parameter;
+    result = params.get(parameterName);
+    return true;
 }
 
 template<typename T>
 T NormalizeBBoxLayer::getParameter(const LayerParams &params,
                                    const std::string &parameterName,
-                                   const size_t &idx)
+                                   const size_t &idx,
+                                   const bool required,
+                                   const T& defaultValue)
 {
-    return getParameterDict(params, parameterName).get<T>(idx);
+    DictValue dictValue;
+    bool success = getParameterDict(params, parameterName, dictValue);
+    if(!success)
+    {
+        if(required)
+        {
+            std::string message = _layerName;
+            message += " layer parameter does not contain ";
+            message += parameterName;
+            message += " parameter.";
+            CV_Error(Error::StsBadArg, message);
+        }
+        else
+        {
+            return defaultValue;
+        }
+    }
+    return dictValue.get<T>(idx);
 }
 
-
 NormalizeBBoxLayer::NormalizeBBoxLayer(LayerParams &params) : Layer(params)
 {
-    _eps = getParameter<float>(params, "eps");
+    _eps = getParameter<float>(params, "eps", 0, false, 1e-10);
     _across_spatial = getParameter<bool>(params, "across_spatial");
     _channel_shared = getParameter<bool>(params, "channel_shared");
 }
@@ -98,7 +104,7 @@ NormalizeBBoxLayer::NormalizeBBoxLayer(LayerParams &params) : Layer(params)
 void NormalizeBBoxLayer::checkInputs(const std::vector<Blob*> &inputs)
 {
     CV_Assert(inputs.size() > 0);
-    for (size_t i = 0; i < inputs.size(); i++)
+    for (size_t i = 1; i < inputs.size(); i++)
     {
         for (size_t j = 0; j < _numAxes; j++)
         {
diff --git a/modules/dnn/src/layers/normalize_bbox_layer.hpp b/modules/dnn/src/layers/normalize_bbox_layer.hpp
index edac1feec..ef2fc884b 100644
--- a/modules/dnn/src/layers/normalize_bbox_layer.hpp
+++ b/modules/dnn/src/layers/normalize_bbox_layer.hpp
@@ -82,11 +82,15 @@ public:
     void checkInputs(const std::vector<Blob*> &inputs);
 
     template<typename T>
-    T getParameter(const LayerParams &params, const std::string &parameterName,
-                   const size_t &idx = 0);
+    T getParameter(const LayerParams &params,
+                   const std::string &parameterName,
+                   const size_t &idx = 0,
+                   const bool required = true,
+                   const T& defaultValue = T());
 
-    DictValue getParameterDict(const LayerParams &params,
-                               const std::string &parameterName);
+    bool getParameterDict(const LayerParams &params,
+                          const std::string &parameterName,
+                          DictValue& result);
 };
 }
 }
diff --git a/modules/dnn/src/layers/prior_box_layer.cpp b/modules/dnn/src/layers/prior_box_layer.cpp
index 817c97473..289f71a70 100644
--- a/modules/dnn/src/layers/prior_box_layer.cpp
+++ b/modules/dnn/src/layers/prior_box_layer.cpp
@@ -53,42 +53,52 @@ namespace dnn
 
 const std::string PriorBoxLayer::_layerName = std::string("PriorBox");
 
-DictValue PriorBoxLayer::getParameterDict(const LayerParams &params,
-                                          const std::string &parameterName)
+bool PriorBoxLayer::getParameterDict(const LayerParams &params,
+                                     const std::string &parameterName,
+                                     DictValue& result)
 {
     if (!params.has(parameterName))
     {
-        std::string message = _layerName;
-        message += " layer parameter does not contain ";
-        message += parameterName;
-        message += " index.";
-        CV_Error(Error::StsBadArg, message);
+        return false;
     }
 
-    DictValue parameter = params.get(parameterName);
-    if(parameter.size() != 1)
-    {
-        std::string message = parameterName;
-        message += " field in ";
-        message += _layerName;
-        message += " layer parameter is required";
-        CV_Error(Error::StsBadArg, message);
-    }
-
-    return parameter;
+    result = params.get(parameterName);
+    return true;
 }
 
 template<typename T>
 T PriorBoxLayer::getParameter(const LayerParams &params,
                               const std::string &parameterName,
-                              const size_t &idx)
+                              const size_t &idx,
+                              const bool required,
+                              const T& defaultValue)
 {
-    return getParameterDict(params, parameterName).get<T>(idx);
+    DictValue dictValue;
+    bool success = getParameterDict(params, parameterName, dictValue);
+    if(!success)
+    {
+        if(required)
+        {
+            std::string message = _layerName;
+            message += " layer parameter does not contain ";
+            message += parameterName;
+            message += " parameter.";
+            CV_Error(Error::StsBadArg, message);
+        }
+        else
+        {
+            return defaultValue;
+        }
+    }
+    return dictValue.get<T>(idx);
 }
 
 void PriorBoxLayer::getAspectRatios(const LayerParams &params)
 {
-    DictValue aspectRatioParameter = getParameterDict(params, "aspect_ratio");
+    DictValue aspectRatioParameter;
+    bool aspectRatioRetieved = getParameterDict(params, "aspect_ratio", aspectRatioParameter);
+    CV_Assert(aspectRatioRetieved);
+
     for (int i = 0; i < aspectRatioParameter.size(); ++i)
     {
         float aspectRatio = aspectRatioParameter.get<float>(i);
@@ -115,7 +125,10 @@ void PriorBoxLayer::getAspectRatios(const LayerParams &params)
 
 void PriorBoxLayer::getVariance(const LayerParams &params)
 {
-    DictValue varianceParameter = getParameterDict(params, "variance");
+    DictValue varianceParameter;
+    bool varianceParameterRetrieved = getParameterDict(params, "variance", varianceParameter);
+    CV_Assert(varianceParameterRetrieved);
+
     int varianceSize = varianceParameter.size();
     if (varianceSize > 1)
     {
@@ -190,12 +203,9 @@ void PriorBoxLayer::allocate(const std::vector<Blob*> &inputs, std::vector<Blob>
     // 2 channels. First channel stores the mean of each prior coordinate.
     // Second channel stores the variance of each prior coordinate.
     size_t outChannels = 2;
-    size_t outHeight = _layerHeight;
-    size_t outWidth = _layerWidth * _numPriors * 4;
-
     _outChannelSize = _layerHeight * _layerWidth * _numPriors * 4;
 
-    outputs[0].create(BlobShape(outNum, outChannels, outHeight, outWidth));
+    outputs[0].create(BlobShape(outNum, outChannels, _outChannelSize));
 }
 
 void PriorBoxLayer::forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs)
diff --git a/modules/dnn/src/layers/prior_box_layer.hpp b/modules/dnn/src/layers/prior_box_layer.hpp
index 13938a607..e398aa165 100644
--- a/modules/dnn/src/layers/prior_box_layer.hpp
+++ b/modules/dnn/src/layers/prior_box_layer.hpp
@@ -83,11 +83,15 @@ public:
     void forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
 
     template<typename T>
-    T getParameter(const LayerParams &params, const std::string &parameterName,
-                   const size_t &idx = 0);
+    T getParameter(const LayerParams &params,
+                   const std::string &parameterName,
+                   const size_t &idx = 0,
+                   const bool required = true,
+                   const T& defaultValue = T());
 
-    DictValue getParameterDict(const LayerParams &params,
-                               const std::string &parameterName);
+    bool getParameterDict(const LayerParams &params,
+                          const std::string &parameterName,
+                          DictValue& result);
 
     void getAspectRatios(const LayerParams &params);
     void getVariance(const LayerParams &params);