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

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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.