A Spatial Prior-Guided Feature Enhancement and Multi-Branch Complementary Learning Framework for Small Ship Detection in SAR Images

Ship detection in Synthetic Aperture Radar (SAR) imagery is essential for maritime surveillance and situational awareness. Despite the advances of deep learning for SAR ship detection, small-target detection is still hindered by severe feature degradation from repeated down-sampling and insufficiently discriminative representations under weak scattering and complex background clutter. To alleviate this dilemma, a Spatial Prior-guided feature enhancement and Multi-branch Complementary learning framework is proposed, termed SPMC, for small ship detection in SAR images. Specifically, a Spatial Prior-Guided Feature Enhancement (SPFE) module is designed to derive spatial attention priors for multi-scale features from ground-truth annotations, thereby emphasizing target-related responses and strengthening small-ship representations. Second, a Multi-branch Complementary Classification (MCC) module is developed, which introduces multiple auxiliary classification heads to learn complementary discriminative information from different classification perspectives. Furthermore, a dual-weighted complementary regularization strategy is proposed to encourages different classifiers to focus on hard samples, thereby improving the discriminative capability for small ships. Extensive experiments on the HRSID and LS-SSDD benchmarks validate the effectiveness of the proposed framework. For extremely small ships with very limited image coverage, SPMC improves the baseline YOLOv11 detector by 3.25%/0.99% in AP50/AP0.5:0.95 on LS-SSDD and by 1.28%/1.21% on HRSID, demonstrating its effectiveness in challenging SAR small ship detection.