CESNet: Cross-dimensional information extraction and channel sharing

To improve detection accuracy, it proposes cross-dimensional information extraction and channel sharing CESNet . The cross-dimensional information extraction CE module uses max pooling and average pooling to strengthen important features in different dimensions, and then interacts across chan-nels to focus on regions of interest. Channel sharing CS module of involu-tion, group convolution and efficient channel attention for deep convolu-tional neural networks ECA-Net . And it can reduce the loss of semantic in-formation caused by channel reduction during feature fusion. Experiments show that the proposed method can work on different networks. Among them, the accuracy of CESNet reaches 34.1 in box AP on COCO dataset. And the detection performance of our network is better than other networks.