diff --git a/paddle/fluid/operators/detection/box_clip_op.cu b/paddle/fluid/operators/detection/box_clip_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..f10c92366de4a1306eedbc193b14dfd674560b9e
--- /dev/null
+++ b/paddle/fluid/operators/detection/box_clip_op.cu
@@ -0,0 +1,74 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <algorithm>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/detection/box_clip_op.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTenso = framework::LoDTensor;
+
+static constexpr int ImInfoSize = 3;
+
+template <typename T, int BlockSize>
+static __global__ void GPUBoxClip(const T *input, const size_t *lod,
+                                  const size_t width, const T *im_info,
+                                  T *output) {
+  for (int i = threadIdx.x; i < (lod[blockIdx.x + 1] - lod[blockIdx.x]) * width;
+       i += BlockSize) {
+    int idx = lod[blockIdx.x] * width + i;
+    T im_w = round(im_info[blockIdx.x * ImInfoSize + 1] /
+                   im_info[blockIdx.x * ImInfoSize + 2]);
+    T im_h = round(im_info[blockIdx.x * ImInfoSize] /
+                   im_info[blockIdx.x * ImInfoSize + 2]);
+    T im_size = (idx % 2 == 0) ? im_w : im_h;
+    output[idx] = max(min(input[idx], im_size - 1), T(0.));
+  }
+}
+
+template <typename DeviceContext, typename T>
+class GPUBoxClipKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
+                   "This kernel only runs on GPU device.");
+    auto *input = context.Input<LoDTensor>("Input");
+    auto *im_info = context.Input<Tensor>("ImInfo");
+    auto *output = context.Output<LoDTensor>("Output");
+    const int64_t num = input->dims()[0];
+    const int64_t bbox_width = input->numel() / num;
+    auto lod = input->lod();
+    framework::LoD abs_offset_lod = framework::ToAbsOffset(lod);
+    auto &dev_ctx = context.template device_context<DeviceContext>();
+    auto stream = dev_ctx.stream();
+    const size_t num_lod = lod.back().size() - 1;
+    T *output_data = output->mutable_data<T>(dev_ctx.GetPlace());
+    GPUBoxClip<T, 512><<<num_lod, 512, 0, stream>>>(
+        input->data<T>(), abs_offset_lod[0].CUDAMutableData(dev_ctx.GetPlace()),
+        bbox_width, im_info->data<T>(), output_data);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    box_clip, ops::GPUBoxClipKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::GPUBoxClipKernel<paddle::platform::CUDADeviceContext, double>);
diff --git a/python/paddle/fluid/tests/test_detection.py b/python/paddle/fluid/tests/test_detection.py
index bbc372da1a8f80a0edb0fa33e4bedd20136ce488..4d8f2b1db1f541317187813b3bc85f98479a70fc 100644
--- a/python/paddle/fluid/tests/test_detection.py
+++ b/python/paddle/fluid/tests/test_detection.py
@@ -354,7 +354,8 @@ class TestGenerateProposals(unittest.TestCase):
         data_shape = [20, 64, 64]
         images = fluid.layers.data(
             name='images', shape=data_shape, dtype='float32')
-        im_info = fluid.layers.data(name='im_info', shape=[3], dtype='float32')
+        im_info = fluid.layers.data(
+            name='im_info', shape=[1, 3], dtype='float32')
         anchors, variances = fluid.layers.anchor_generator(
             name='anchor_generator',
             input=images,