SAR Ship Detector Using Cross-stage Feature Fusion and Decoupled Head with Mutual Guidance

Abstract

Deep learning-based SAR ship detection methods enhance resilience to noise, distortion, and interference in ocean environments, establishing them as the foremost approach for ship detection nowadays. Nonetheless, substantial difficulties persist in separating ships from the complex backgrounds found in SAR images: 1) Many ships are highly similar to the sea surface clutter noise, making them susceptible to false alarms; 2) Ship targets exhibit a wide range of variations in size and shape. In this paper, we propose a novel network to address above problems. Firstly, the deformable convolution is incorporated into the backbone network to adapt to the wide-range of ship shapes. Secondly, the cross-stage feature fusion module (CSFFM) is introduced to realize local self-supervised interaction between two adjacent layers, thereby reducing the impact of receptive field differences between different feature layers and mitigating the influence of complex background noise. Finally, the mutually guided decoupled-head (MGDH) is designed to achieve mutual guidance between classification and regression, thus further enhancing the significant regions of the feature maps. Through extensive experiments, it has been verified that our proposed method has achieved the most promising performance compared to well-known baselines. The codes will be available at https://github.com/SYLan2019/CSFF-MGDH.