Dual-Domain Fusion for Small-Object SAR Ship Detection in Complex Scenes

Synthetic aperture radar (SAR) ship detection is essential for maritime surveillance, yet coherent speckle can obscure tiny offshore vessels, while high-response coastal structures can produce target-like responses. Repeated downsampling further weakens sparse vessel signatures, whereas a separate high-resolution prediction head increases dense prediction costs. We propose a dual-domain detector that embeds spatial–frequency encoding, multiscale refinement, and high-resolution feature transfer within the backbone–neck–pyramid representation. Scharr-derived boundary cues are coupled with learned frequency context and remain discriminative as features are fused across scales, while sample-preserving space-to-depth transformation and global–local receptive-field modeling integrate shallow detail into semantically stronger P3 features without introducing a separate P2 prediction head. Coordinated FSEM, MSFE, and SOEP modules preserve fine vessel structure and reduce clutter-induced ambiguity throughout the detection hierarchy. Experiments on SSDD, HRSID, and RSDD-SAR evaluate detection accuracy, cross-dataset transfer, limited-data behavior, sensitivity to controlled perturbations, and edge deployment. Across 30 seed-matched SSDD runs, the method improves mAP50:95 over the baseline by 2.70±0.34 percentage points. It achieves 69.3% and 71.5% mAP50:95 on HRSID and RSDD. On the Atlas 200I platform, the compiled FP16 model retains 69.3% mAP50:95 while running at 101.8 frames/s.