diff --git a/modules/dnn/test/test_halide_layers.cpp b/modules/dnn/test/test_halide_layers.cpp
index eda414551f..637b1f00b5 100644
--- a/modules/dnn/test/test_halide_layers.cpp
+++ b/modules/dnn/test/test_halide_layers.cpp
@@ -16,7 +16,7 @@ using namespace cv;
 using namespace cv::dnn;
 using namespace testing;
 
-static void test(Mat& input, Net& net, int backendId, int targetId)
+static void test(Mat& input, Net& net, Backend backendId, Target targetId)
 {
     DNNTestLayer::checkBackend(backendId, targetId);
     randu(input, -1.0f, 1.0f);
@@ -34,7 +34,7 @@ static void test(Mat& input, Net& net, int backendId, int targetId)
     normAssert(outputDefault, outputHalide, "", l1, lInf);
 }
 
-static void test(LayerParams& params, Mat& input, int backendId, int targetId)
+static void test(LayerParams& params, Mat& input, Backend backendId, Target targetId)
 {
     Net net;
     net.addLayerToPrev(params.name, params.type, params);
@@ -101,8 +101,8 @@ TEST_P(Convolution, Accuracy)
     Size pad = get<4>(GetParam());
     Size dilation = get<5>(GetParam());
     bool hasBias = get<6>(GetParam());
-    int backendId = get<0>(get<7>(GetParam()));
-    int targetId = get<1>(get<7>(GetParam()));
+    Backend backendId = get<0>(get<7>(GetParam()));
+    Target targetId = get<1>(get<7>(GetParam()));
 
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
         throw SkipTestException("");
@@ -171,8 +171,8 @@ TEST_P(Deconvolution, Accuracy)
     Size stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]);
     Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]);
     bool hasBias = get<6>(GetParam());
-    int backendId = get<0>(get<7>(GetParam()));
-    int targetId = get<1>(get<7>(GetParam()));
+    Backend backendId = get<0>(get<7>(GetParam()));
+    Target targetId = get<1>(get<7>(GetParam()));
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU &&
         dilation.width == 2 && dilation.height == 2)
         throw SkipTestException("");
@@ -235,8 +235,8 @@ TEST_P(LRN, Accuracy)
     float bias = get<2>(GetParam())[2];
     bool normBySize = get<3>(GetParam());
     std::string nrmType = get<4>(GetParam());
-    int backendId = get<0>(get<5>(GetParam()));
-    int targetId = get<1>(get<5>(GetParam()));
+    Backend backendId = get<0>(get<5>(GetParam()));
+    Target targetId = get<1>(get<5>(GetParam()));
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
         throw SkipTestException("");
 
@@ -276,8 +276,8 @@ TEST_P(AvePooling, Accuracy)
     Size outSize = get<1>(GetParam());;  // Input size will be computed from parameters.
     Size kernel = get<2>(GetParam());
     Size stride = get<3>(GetParam());
-    int backendId = get<0>(get<4>(GetParam()));
-    int targetId = get<1>(get<4>(GetParam()));
+    Backend backendId = get<0>(get<4>(GetParam()));
+    Target targetId = get<1>(get<4>(GetParam()));
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD)
         throw SkipTestException("");
 
@@ -317,8 +317,8 @@ TEST_P(MaxPooling, Accuracy)
     Size kernel = get<2>(GetParam());
     Size stride = get<3>(GetParam());
     Size pad = get<4>(GetParam());
-    int backendId = get<0>(get<5>(GetParam()));
-    int targetId = get<1>(get<5>(GetParam()));
+    Backend backendId = get<0>(get<5>(GetParam()));
+    Target targetId = get<1>(get<5>(GetParam()));
 
     LayerParams lp;
     lp.set("pool", "max");
@@ -355,8 +355,8 @@ TEST_P(FullyConnected, Accuracy)
     Size inSize = get<1>(GetParam());
     int outChannels = get<2>(GetParam());
     bool hasBias = get<3>(GetParam());
-    int backendId = get<0>(get<4>(GetParam()));
-    int targetId = get<1>(get<4>(GetParam()));
+    Backend backendId = get<0>(get<4>(GetParam()));
+    Target targetId = get<1>(get<4>(GetParam()));
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
         throw SkipTestException("");
 
@@ -394,8 +394,8 @@ typedef TestWithParam<tuple<int,  tuple<Backend, Target> > > SoftMax;
 TEST_P(SoftMax, Accuracy)
 {
     int inChannels = get<0>(GetParam());
-    int backendId = get<0>(get<1>(GetParam()));
-    int targetId = get<1>(get<1>(GetParam()));
+    Backend backendId = get<0>(get<1>(GetParam()));
+    Target targetId = get<1>(get<1>(GetParam()));
     LayerParams lp;
     lp.type = "SoftMax";
     lp.name = "testLayer";
@@ -457,7 +457,7 @@ TEST_P(Test_Halide_layers, MaxPoolUnpool)
 ////////////////////////////////////////////////////////////////////////////////
 static const int kNumChannels = 3;
 
-void testInPlaceActivation(LayerParams& lp, int backendId, int targetId)
+void testInPlaceActivation(LayerParams& lp, Backend backendId, Target targetId)
 {
     EXPECT_FALSE(lp.name.empty());
 
@@ -485,8 +485,8 @@ TEST_P(BatchNorm, Accuracy)
     bool hasWeights = get<0>(GetParam());
     bool hasBias = get<1>(GetParam());
     float epsilon = get<2>(GetParam());
-    int backendId = get<0>(get<3>(GetParam()));
-    int targetId = get<1>(get<3>(GetParam()));
+    Backend backendId = get<0>(get<3>(GetParam()));
+    Target targetId = get<1>(get<3>(GetParam()));
 
     LayerParams lp;
     lp.set("has_weight", hasWeights);
@@ -518,8 +518,8 @@ typedef TestWithParam<tuple<float, tuple<Backend, Target> > > ReLU;
 TEST_P(ReLU, Accuracy)
 {
     float negativeSlope = get<0>(GetParam());
-    int backendId = get<0>(get<1>(GetParam()));
-    int targetId = get<1>(get<1>(GetParam()));
+    Backend backendId = get<0>(get<1>(GetParam()));
+    Target targetId = get<1>(get<1>(GetParam()));
 
     LayerParams lp;
     lp.set("negative_slope", negativeSlope);
@@ -536,8 +536,8 @@ INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Combine(
 typedef TestWithParam<tuple<std::string, tuple<Backend, Target> > > NoParamActivation;
 TEST_P(NoParamActivation, Accuracy)
 {
-    int backendId = get<0>(get<1>(GetParam()));
-    int targetId = get<1>(get<1>(GetParam()));
+    Backend backendId = get<0>(get<1>(GetParam()));
+    Target targetId = get<1>(get<1>(GetParam()));
 
     LayerParams lp;
     lp.type = get<0>(GetParam());
@@ -555,8 +555,8 @@ TEST_P(Power, Accuracy)
     float power = get<0>(GetParam())[0];
     float scale = get<0>(GetParam())[1];
     float shift = get<0>(GetParam())[2];
-    int backendId = get<0>(get<1>(GetParam()));
-    int targetId = get<1>(get<1>(GetParam()));
+    Backend backendId = get<0>(get<1>(GetParam()));
+    Target targetId = get<1>(get<1>(GetParam()));
 
     LayerParams lp;
     lp.set("power", power);
@@ -589,8 +589,8 @@ typedef TestWithParam<tuple<bool, tuple<Backend, Target> > > Scale;
 TEST_P(Scale, Accuracy)
 {
     bool hasBias = get<0>(GetParam());
-    int backendId = get<0>(get<1>(GetParam()));
-    int targetId = get<1>(get<1>(GetParam()));
+    Backend backendId = get<0>(get<1>(GetParam()));
+    Target targetId = get<1>(get<1>(GetParam()));
 
     LayerParams lp;
     lp.set("bias_term", hasBias);
@@ -624,8 +624,8 @@ TEST_P(Concat, Accuracy)
 {
     Vec3i inSize = get<0>(GetParam());
     Vec3i numChannels = get<1>(GetParam());
-    int backendId = get<0>(get<2>(GetParam()));
-    int targetId = get<1>(get<2>(GetParam()));
+    Backend backendId = get<0>(get<2>(GetParam()));
+    Target targetId = get<1>(get<2>(GetParam()));
 
     Net net;
 
@@ -692,8 +692,8 @@ TEST_P(Eltwise, Accuracy)
     std::string op = get<1>(GetParam());
     int numConv = get<2>(GetParam());
     bool weighted = get<3>(GetParam());
-    int backendId = get<0>(get<4>(GetParam()));
-    int targetId = get<1>(get<4>(GetParam()));
+    Backend backendId = get<0>(get<4>(GetParam()));
+    Target targetId = get<1>(get<4>(GetParam()));
 
     Net net;