YOLO-RSD: Enhanced Rotated Ship Detection in SAR Images

Rotated ship detection in synthetic aperture radar (SAR) imagery remains challenging due to arbitrary orientations, large scale variations, and strong sea clutter. This paper presents YOLO-RSD, a lightweight yet effective oriented object detector tailored for maritime surveillance. We first introduce a Feature-Aligned Spatial Attention (FASA) backbone that integrates deformable convolution with spatial attention to enhance orientation- and scale-adaptive feature extraction. Then, a FuseDiff neck module is designed to achieve consistent cross-scale semantic fusion via multi-branch convolution and context diffusion. Finally, a Task-Aligned Dynamic Detection Head (TDDH) is constructed using task-specific attention to reconcile classification–localization conflicts. Experiments on SSDD+ and RSDD-SAR datasets demonstrate that YOLO-RSD achieves state-of-the-art performance with only 2.61M parameters, outperforming recent YOLO variants and other comparative models in both accuracy and efficiency. The proposed framework offers a general and hardware-friendly paradigm for robust rotated target detection in SAR imagery.