diff --git a/modules/dnn/perf/perf_net.cpp b/modules/dnn/perf/perf_net.cpp
index 038a12fed9..0d6d257559 100644
--- a/modules/dnn/perf/perf_net.cpp
+++ b/modules/dnn/perf/perf_net.cpp
@@ -87,6 +87,7 @@ public:
         size_t weightsMemory = 0, blobsMemory = 0;
         net.getMemoryConsumption(netInputShape, weightsMemory, blobsMemory);
         int64 flops = net.getFLOPS(netInputShape);
+        CV_Assert(flops > 0);
 
         net.forward(outputLayer); // warmup
 
diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp
index 579b8f5a5c..d34ee9856e 100644
--- a/modules/dnn/src/layers/convolution_layer.cpp
+++ b/modules/dnn/src/layers/convolution_layer.cpp
@@ -1022,7 +1022,7 @@ public:
         int64 flops = 0;
         for (int i = 0; i < inputs.size(); i++)
         {
-            flops += total(outputs[i])*(2*kernel.area()*inputs[i][1] + 1);
+            flops += total(outputs[i])*(CV_BIG_INT(2)*kernel.area()*inputs[i][1] + 1);
         }
 
         return flops;
@@ -1440,7 +1440,7 @@ public:
 
         for (int i = 0; i < inputs.size(); i++)
         {
-            flops += 2*outChannels*kernel.area()*total(inputs[i]);
+            flops += CV_BIG_INT(2)*outChannels*kernel.area()*total(inputs[i]);
         }
 
         return flops;
diff --git a/modules/dnn/src/layers/fully_connected_layer.cpp b/modules/dnn/src/layers/fully_connected_layer.cpp
index 59d8ed935b..ab18c54151 100644
--- a/modules/dnn/src/layers/fully_connected_layer.cpp
+++ b/modules/dnn/src/layers/fully_connected_layer.cpp
@@ -397,7 +397,7 @@ public:
         int innerSize = blobs[0].size[1];
         for(int i = 0; i < outputs.size(); i++)
         {
-            flops += 3*innerSize*total(outputs[i]);
+            flops += CV_BIG_INT(3)*innerSize*total(outputs[i]);
         }
 
         return flops;