diff --git a/modules/dnn/include/opencv2/dnn/dnn.hpp b/modules/dnn/include/opencv2/dnn/dnn.hpp
index 59970e6dd4919c382221cf5b89eda3f3cf05eca0..b92ecc0fa601b0c3c8d40ca94797249938a485ed 100644
--- a/modules/dnn/include/opencv2/dnn/dnn.hpp
+++ b/modules/dnn/include/opencv2/dnn/dnn.hpp
@@ -695,12 +695,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      *  @param scalefactor multiplier for @p image values.
      *  @param swapRB flag which indicates that swap first and last channels
      *  in 3-channel image is necessary.
-     *  @details input image is resized so one side after resize is equal to corresponing
+     *  @param crop flag which indicates whether image will be cropped after resize or not
+     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponing
      *  dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
+     *  If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
      *  @returns 4-dimansional Mat with NCHW dimensions order.
      */
     CV_EXPORTS_W Mat blobFromImage(const Mat& image, double scalefactor=1.0, const Size& size = Size(),
-                                   const Scalar& mean = Scalar(), bool swapRB=true);
+                                   const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
     /** @brief Creates 4-dimensional blob from series of images. Optionally resizes and
      *  crops @p images from center, subtract @p mean values, scales values by @p scalefactor,
      *  swap Blue and Red channels.
@@ -711,12 +713,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
      *  @param scalefactor multiplier for @p images values.
      *  @param swapRB flag which indicates that swap first and last channels
      *  in 3-channel image is necessary.
-     *  @details input image is resized so one side after resize is equal to corresponing
+     *  @param crop flag which indicates whether image will be cropped after resize or not
+     *  @details if @p crop is true, input image is resized so one side after resize is equal to corresponing
      *  dimension in @p size and another one is equal or larger. Then, crop from the center is performed.
+     *  If @p crop is false, direct resize without cropping and preserving aspect ratio is performed.
      *  @returns 4-dimansional Mat with NCHW dimensions order.
      */
     CV_EXPORTS_W Mat blobFromImages(const std::vector<Mat>& images, double scalefactor=1.0,
-                                    Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=true);
+                                    Size size = Size(), const Scalar& mean = Scalar(), bool swapRB=true, bool crop=true);
 
     /** @brief Convert all weights of Caffe network to half precision floating point.
      * @param src Path to origin model from Caffe framework contains single
diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp
index 424e8425a4e82a488a333c3dbdd88424b59fbf09..22592c05a3c9eb773cd1ab9d6cefe2946486dbb7 100644
--- a/modules/dnn/src/dnn.cpp
+++ b/modules/dnn/src/dnn.cpp
@@ -85,15 +85,15 @@ static String toString(const T &v)
 }
 
 Mat blobFromImage(const Mat& image, double scalefactor, const Size& size,
-                  const Scalar& mean, bool swapRB)
+                  const Scalar& mean, bool swapRB, bool crop)
 {
     CV_TRACE_FUNCTION();
     std::vector<Mat> images(1, image);
-    return blobFromImages(images, scalefactor, size, mean, swapRB);
+    return blobFromImages(images, scalefactor, size, mean, swapRB, crop);
 }
 
 Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size size,
-                   const Scalar& mean_, bool swapRB)
+                   const Scalar& mean_, bool swapRB, bool crop)
 {
     CV_TRACE_FUNCTION();
     std::vector<Mat> images = images_;
@@ -104,13 +104,18 @@ Mat blobFromImages(const std::vector<Mat>& images_, double scalefactor, Size siz
             size = imgSize;
         if (size != imgSize)
         {
-            float resizeFactor = std::max(size.width / (float)imgSize.width,
-                                          size.height / (float)imgSize.height);
-            resize(images[i], images[i], Size(), resizeFactor, resizeFactor);
-            Rect crop(Point(0.5 * (images[i].cols - size.width),
-                            0.5 * (images[i].rows - size.height)),
-                      size);
-            images[i] = images[i](crop);
+            if(crop)
+            {
+              float resizeFactor = std::max(size.width / (float)imgSize.width,
+                                            size.height / (float)imgSize.height);
+              resize(images[i], images[i], Size(), resizeFactor, resizeFactor);
+              Rect crop(Point(0.5 * (images[i].cols - size.width),
+                              0.5 * (images[i].rows - size.height)),
+                        size);
+              images[i] = images[i](crop);
+            }
+            else
+              resize(images[i], images[i], size);
         }
         if(images[i].depth() == CV_8U)
             images[i].convertTo(images[i], CV_32F);