DLAHSD: Dynamic Label adopted in Auxiliary Head for SAR Detection

Ship detection in synthetic aperture radar (SAR) images is a major issue in naval surveillance and port management. Existing challenges are mainly as follows: (1) Tiny ships are mixed with scattered noise spots on the sea. (2) Ships come in extreme aspect-ratios and various scales. (3) The land background blurs the outline of coastal ships. To address these problems, we proposed an efficient detection neural network (DLAHSD) that raised the Multi-scale Feature Location Fusion (MFLF) module and the Auxiliary Detection Head (ADH) based CenterNet. In addition, we designed a Dynamic Elliptic Gaussian (DEG) to label the heatmap of ships. Experimental results on the challenging SSDD dataset show that our model offers improved performance over the baseline methods. The codes will be available at {https://github.com/SYLan2019/DLAHSD}