SAR-RAFNet: A SAR Imaging-Aware Resampling and Fusion Network for Ship Detection in Complex Maritime Scenes

Abstract

Ship detection in synthetic aperture radar (SAR) imagery remains challenging in complex maritime scenes. Weak and locally discontinuous ship-related intensity responses may be progressively degraded during multiscale feature transformation. Meanwhile, sea clutter, coastlines, harbor facilities, and other strong background structures may introduce confusing responses during cross-scale feature fusion. Existing detectors commonly inherit fixed downsampling, interpolation-based upsampling, and direct feature concatenation from general object detection frameworks. These generic operations may inadequately preserve contour-sensitive ship structures and may propagate clutter-contaminated features. To address these issues, this article proposes SAR-RAFNet, a SAR imaging-aware resampling and fusion network for ship detection in complex maritime scenes. First, an edge-guided adaptive downsampling (EGADS) module uses neighborhood context and Sobel-derived directional edge priors to guide adaptive four-subcell aggregation. Contour residual compensation further preserves weak, elongated, and contour-sensitive ship-related intensity structures during resolution reduction. Second, the established CARAFE operator is directly adopted in the top-down pathway to improve the spatial coherence of semantic feature reconstruction. Finally, a scattering-aware feature aggregation module (SFAM) explicitly models pairwise discrepancy and consistency between aligned feature streams. It performs branch-wise spatial-channel competitive selection with complementary interaction residual compensation, thereby enhancing mutually supported ship-related responses and suppressing branch-specific clutter. Experiments on SSDD, HRSID, and LS-SSDD-v1.0 demonstrate that SAR-RAFNet improves $\text{mAP}_{50:95}$ over the YOLOv26 baseline by 1.71, 2.13, and 2.17 percentage points, respectively. Ablation studies and visual analyses further verify the complementary contributions of EGADS and SFAM, particularly for small, weak-response, densely distributed, and nearshore ships. These results demonstrate that redesigning multiscale resampling and cross-scale interaction according to SAR intensity-domain characteristics can consistently improve ship detection across diverse maritime scenes.