diff --git a/modules/dnn/src/layers/prior_box_layer.cpp b/modules/dnn/src/layers/prior_box_layer.cpp
index 1c5dd59ef3..967aa7d1fa 100644
--- a/modules/dnn/src/layers/prior_box_layer.cpp
+++ b/modules/dnn/src/layers/prior_box_layer.cpp
@@ -192,8 +192,23 @@ public:
         }
     }
 
-    PriorBoxLayerImpl(const LayerParams &params)
+    static LayerParams normalizeONNXParams(const LayerParams& params)
     {
+        LayerParams p = params;
+        auto remap = [&](const std::string& from, const std::string& to) {
+            if (p.has(from) && !p.has(to))
+                p.set(to, p.get(from));
+        };
+        remap("variances",     "variance");
+        remap("min_sizes",     "min_size");
+        remap("max_sizes",     "max_size");
+        remap("aspect_ratios", "aspect_ratio");
+        return p;
+    }
+
+    PriorBoxLayerImpl(const LayerParams &params_)
+    {
+        const LayerParams params = normalizeONNXParams(params_);
         setParamsFrom(params);
         _flip = getParameter<bool>(params, "flip", 0, false, true);
         _clip = getParameter<bool>(params, "clip", 0, false, true);
diff --git a/modules/dnn/src/onnx/onnx_importer2.cpp b/modules/dnn/src/onnx/onnx_importer2.cpp
index acbd1d4c85..0dde754496 100644
--- a/modules/dnn/src/onnx/onnx_importer2.cpp
+++ b/modules/dnn/src/onnx/onnx_importer2.cpp
@@ -189,6 +189,7 @@ protected:
     void parseCumSum               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseDepthSpaceOps        (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseDetectionOutput      (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
+    void parsePriorBox             (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseEinsum               (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseElementWise          (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
     void parseElu                  (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
@@ -1913,6 +1914,17 @@ void ONNXImporter2::parseSoftMax(LayerParams& layerParams, const opencv_onnx::No
 void ONNXImporter2::parseDetectionOutput(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
 {
     CV_CheckEQ(node_proto.input_size(), 3, "");
+    if (layerParams.has("code_type"))
+    {
+        int ct = layerParams.get<int>("code_type");
+        layerParams.set("code_type", ct == 2 ? "CENTER_SIZE" : "CORNER");
+    }
+    addLayer(layerParams, node_proto);
+}
+
+void ONNXImporter2::parsePriorBox(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto)
+{
+    layerParams.type = "PriorBox";
     addLayer(layerParams, node_proto);
 }
 
@@ -2759,6 +2771,7 @@ void ONNXImporter2::buildDispatchMap_ONNX_AI()
     dispatch["NonMaxSuprression"] = &ONNXImporter2::parseNonMaxSuprression;
     dispatch["SoftMax"] = dispatch["Softmax"] = dispatch["LogSoftmax"] = &ONNXImporter2::parseSoftMax;
     dispatch["DetectionOutput"] = &ONNXImporter2::parseDetectionOutput;
+    dispatch["PriorBox"] = &ONNXImporter2::parsePriorBox;
     dispatch["CumSum"] = &ONNXImporter2::parseCumSum;
     dispatch["SpaceToDepth"] = dispatch["DepthToSpace"] = &ONNXImporter2::parseDepthSpaceOps;
     dispatch["ScatterElements"] = dispatch["Scatter"] = dispatch["ScatterND"] = &ONNXImporter2::parseScatter;
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index 6f2687842d..29fcc27160 100644
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -1179,6 +1179,11 @@ TEST_P(Test_ONNX_layers, Resize_HumanSeg)
     testONNXModels("resize_humanseg");
 }
 
+TEST_P(Test_ONNX_layers, Resample)
+{
+    testONNXModels("nearest", npy, 0, 0, false, false);
+}
+
 TEST_P(Test_ONNX_layers, Div)
 {
     const String model =  _tf("models/div.onnx");
@@ -2293,6 +2298,31 @@ TEST_P(Test_ONNX_layers, QLinearSoftmax)
     testONNXModels("qlinearsoftmax_v13", npy, 0.002, 0.002);
 }
 
+TEST_P(Test_ONNX_layers, PriorBox_ONNX)
+{
+    Net net = readNetFromONNX(_tf("models/prior_box.onnx"));
+    ASSERT_FALSE(net.empty());
+    int inp_size[] = {1, 3, 10, 10};
+    int shape_size[] = {1, 2, 3, 4};
+    Mat inp(4, inp_size, CV_32F, Scalar(0));
+    Mat shape(4, shape_size, CV_32F, Scalar(0));
+    net.setInput(inp, "input_0");
+    net.setInput(shape, "input_1");
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
+    Mat out = net.forward();
+    Mat ref = blobFromNPY(_tf("data/output_prior_box.npy"));
+
+    double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-5;
+    double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-3 : 1e-4;
+    if (target == DNN_TARGET_CUDA_FP16)
+    {
+        l1 = 7e-5;
+        lInf = 0.0005;
+    }
+    normAssert(out, ref, "", l1, lInf);
+}
+
 INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
 
 class Test_ONNX_nets : public Test_ONNX_layers