RGCNet: A Lightweight Radiometric–Geometric–Contextual Network for SAR Oil Spill Detection in Maritime Monitoring

Marine oil spills pose serious threats to coastal ecosystems and maritime activities, and synthetic aperture radar (SAR) has become an important tool for all-weather marine monitoring. However, SAR oil spill detection remains challenging because oil spills usually appear as weak dark anomalies with blurred boundaries, elongated or fragmented shapes, and strong interference from lookalike phenomena such as low-wind areas and internal waves. To address these issues, we propose RGCNet, a lightweight radiometric–geometric–contextual detection framework based on YOLOv11n. Firstly, the H_SPDRFF module is incorporated into the backbone to enhance weak radiometric responses through constrained feature amplification, thereby reducing missed detections caused by low-contrast oil slicks. Secondly, the C3k2_GSR module is designed in the neck to strengthen anisotropic geometric refinement and preserve the continuity of elongated and fragmented oil spill regions during multi-scale feature fusion. Finally, a SAR-adapted large selective kernel block (LSKBlock) is embedded in the high-level backbone to improve contextual discrimination between true oil spills and lookalike dark formations. Experiments on DeepSAR show that RGCNet increases mAP@0.5 and mAP@0.5:0.95 by 3.6 and 3.0 percentage points over the YOLOv11n baseline, respectively. Cross-dataset evaluation on SAR-Oil-Spill demonstrates a 3.9-point mAP@0.5 gain, indicating strong transferability. Furthermore, with a compact model size of 2.67 M parameters and 6.4 G FLOPs, RGCNet achieves an inference speed of 162.5 FPS on an RTX A4000 GPU, demonstrating its efficiency and potential for real-time maritime surveillance. Nevertheless, the current bounding-box formulation cannot precisely delineate irregular oil-spill boundaries. Future work will therefore investigate fine-grained segmentation and cross-sensor adaptation.