LDSS-NET: A LIGHTWEIGHT NETWORK FOR DENSE SAR SHIP DETECTION

Deep learning has been extensively applied in SAR ship detection because of its powerful feature extraction ability. However, most methods are not only complex, but also easily lead to missed and false detections when the ships are arranged densely. To meet above challenges, a lightweight network LDSS-Net for dense SAR ship detection is proposed. Firstly, we improve the CSP structure and design a lightweight gradient shunt aggregation backbone network LGSA for better feature extraction while reducing computational overhead. Secondly, a feature fusion network DCE-PAN is proposed for enhancing dense ship contour, which enriches the frequency domain information and improves the local feature correlation by using DWT and ECA. Experiments on public datasets SSDD and HRSID demonstrate that the mAP of LDSS-Net reaches 98.20% and 91.29%, respectively, and the parameters are only 2.0M. Our network outperforms existing advanced networks and achieves excellent detection results.