Multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection

In complex background, the accuracy of moving object detection can be affected by some factors such as illumination change, short occlusion and background movement. This paper proposes a new multi-scale information fusion based on convolution kernel pyramid and dilated convolution for Wushu moving object detection. The proposed model uses a variety of ways to fuse the feature information. First, the multi-layer feature map information with different sizes is fused by the per-pixel addition method. Then the feature map of different stages is splicing in the channel dimension to form the information fusion feature layer with rich semantic information and detail information as the prediction layer of the model. In this model, convolution kernel pyramid structure is introduced into the anchor frame mechanism to solve the multi-scale problem of detecting objects. The number of parameters increased by large convolution kernel is reduced by using dilated convolution to reduce the number of anchor frames reasonably. Experimental results show that the proposed fusion algorithm has certain anti-interference ability and high precision for moving object detection in complex environment compared the state-of-the-art methods.