GUS-YOLO Remote Sensing Target Detection Algorithm Introducing Context Information and Attention Gate

Abstract

At present, there are still some problems in the remote sensing target detection algorithm based on the general YOLO (you only look once) series, such as not making full use of the global context information of the image, not narrowing the semantic gap in the feature fusion pyramid part, and not suppressing the interference of redundant information. On the basis of combining the advantages of YOLO algorithms, this paper proposes GUS-YOLO (network of global context extraction unit and attention gate-based YOLOS) algorithm. It has a backbone network Global Backbone that can make full use of global context information. Other than that, this algorithm introduces the Attention Gate module into the top-down structure of the fused feature pyramid, which can emphasize the necessary feature information and suppress redundant information. Furthermore, this paper designs the best network structure for the Attention Gate module and proposes the feature fusion structure U-Net of proposed network.  Finally, because the ReLU activation function may lead to the problem that the model gradient is no longer updated, the Attention Gate module uses a learnable SMU (smooth maximum unit) activation function, which can improve the robustness of the model. On the NWPU VHR-10 remote sensing dataset, this algorithm achieves 1.64 percentage points and 9.39 percentage points performance improvement on mAP0.50 and mAP0.75 respectively compared with YOLOV7. Compared with the current 7 mainstream detection algorithms, this algorithm achieves better detection performance.

Keywords

• remote sensing image; global backbone; attention gate; smu; u-neck