Gl-net:gaussian leading network for sar ship detection

Ship detection in synthetic aperture radar (SAR) images is a basic and challenging task in marine monitoring. There has been made remarkable achievements in recent years. However, the existing detection methods still have the problem of ambiguous location information. It leads to the lack of model pertinence, and the poor discrimination of foreground and neighboring background. In this paper, we propose a ship detection method based on FSAF. In particular, we design a Gaussian Leading Block (GLB) capable to eliminate the interference of neighboring background information in the ground truth. Experiments on the HRSID dataset show that the proposed method achieves a 3.6% Average Precision (AP) improvement over the baseline at a low cost.