Multiscale and Dense Ship Detection in SAR Images Based on Key-Point Estimation and Attention Mechanism

Abstract

Ship target detection in synthetic aperture radar (SAR) images is essential for many applications in marine monitoring and port security. Though considerable developments have been achieved, there still exist some issues toward multiscale and dense ship targets in complex inshore scenes. Under such common but challenging situations, it is difficult to extract effective target information, which drives the missing alarm rate rising dramatically. In complex scenes, it is hard to disentangle background noise from ship target information, which causes false alarm frequently. In this article, an anchor-free SAR ship detection method based on key-point estimation and attention mechanism is proposed to address the aforementioned issues. Specially, an anchor-free framework with skip connections and aggregation nodes is designed to fuse multiresolution features and detect multiscale ship targets. Moreover, a key-point estimation module is proposed to eliminate the undetected ship targets caused by dense target distribution. Furthermore, a channel attention module is explored to enhance network attention on ship targets and suppress background noise. Sufficient experimental results on the open SAR ship detection dataset demonstrate that compared with some state-of-the-art methods, the proposed method is able to achieve higher detection accuracy with a lower false alarm rate.