Robust ship detection in adverse weather via architecture and loss redesign

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Abstract
Ship detection is essential for safe maritime and inland-waterway navigation and port logistics, yet adverse weather (rain, snow, fog) degrades visibility and increases missed detections and false alarms. This study proposes Robust Ship-detection YOLO for Adverse Weather (RSYA), an enhanced real-time detector for maritime surveillance in degraded environments. As an artificial intelligence contribution, RSYA augments a You Only Look Once version 11 nano (YOLO11n) baseline by adding a high-resolution P2 detection head for small vessels, introducing C3k2-Triplet blocks to strengthen feature representation, and using Focal-Complete Intersection over Union loss to emphasize hard, small targets. As an engineering application, we generate and evaluate on adverse-weather ship imagery with controlled rain, snow, and fog corruption, together with corresponding original-weather data. Across comparative experiments, RSYA improves robustness while preserving real-time throughput. On the adverse-weather dataset, RSYA increases recall by 2.90%, mean Average Precision at Intersection over Union 0.5 (mAP_{50}) by 2.29%, and mean Average Precision averaged over thresholds 0.5–0.95 (mAP_{50-95}) by 3.35% over the YOLO11n baseline. These results show that combining weather-oriented data synthesis with targeted architectural and loss redesign substantially improves ship-detection performance under degraded visibility.