Ship detection in SAR image based on improved YOLOv5 network

In order to improve the accuracy of YOLO series algorithm in detecting small ship targets in SAR images, a target detection algorithm based on improved yolov5 is proposed in this paper. In this paper, The Multi-Scale Channel Attention Module (MS_CAM) is added to the network structure to aggregate local and global feature information in the way of channel attention, which can alleviate the problem of large semantic gap between different scales to a certain extent. In addition, the PANet fusion structure in YOLOv5 was replaced by BiFPN structure to make the network better weight of learning features. The experiment on the open RSDD-SAR dataset shows that compared with the traditional method, the AP value and recall rate of the whole dataset are improved.