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Unmanned Aerial Vehicle (UAV) remote sensing images provide high-resolution and flexible monitoring data for oil spill detection. To address the high computational cost and low accuracy of traditional models, this study proposes an improved model, YOLOv8m-CGSE. The model replaces standard convolution with Group Shuffle Convolution (GSConv), substitutes the C2f module with SENetV2, and introduces a light-weight Cross-scale Context Fusion Module (CCFM) to enhance multi-scale feature representation while maintaining a lightweight structure. Mosaic augmentation was applied to the marine oil spill dataset, improving mAP50 and mAP50–95 to 85.4% and 62.0%, respectively. Based on YOLOv8m, the proposed YOLOv8m-CGSE achieved mAP50 and mAP50–95 of 91.2% and 73.3%, respectively, improving accuracy while reducing parameters by 16.1% and computational cost by 12.6%. Furthermore, a supplementary vulnerability test on highly deceptive oil-free sea surfaces demonstrated that the proposed model actively suppresses complex background clutter (e.g., ship wakes and wave anomalies), effectively reducing false positive detections from 21 (baseline) to 15. The results demonstrate that the proposed model effectively balances high precision, robustness against visual lookalikes and computational efficiency for real-time marine oil spill monitoring.
Wang et al. (Fri,) studied this question.