Ship Grid: A Novel Anchor-Free Ship Detection Algorithm

Video-based ship detection is crucial for the real-time monitoring of maritime activities, aiding decision making in maritime traffic management, safety monitoring, and rescue operations. Current challenges include multiscale variations and occlusion issues affecting detection accuracy. Existing ship detection methods often address the multiscale problem by redesigning the network architecture, providing limited improvements. We present Ship Grid, an innovative anchor-free ship detection algorithm. Ship Grid tackles the challenges of ship feature capture in occluded scenarios by directly generating bounding boxes at the predicted centers during the label assignment phase. Moreover, it enables simultaneous ship feature extraction at multiple scales, effectively addressing the issues of insufficient feature extraction for small objects and imprecise localization for large objects caused by stark scale variations. In the bounding box regression phase, we introduce a scale-invariant localization loss that guides the regression process of prediction boxes at different scales. This approach allows the network to comprehensively learn ship features across multiple scales and further enhances performance in the presence of large ship scale variations. We rigorously evaluated the ship grid on the SeaShips dataset, achieving 0.988 and 0.835 on the evaluation metrics of mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 and mAP at IoU thresholds ranging from 0.5 to 0.95 This outperforms state-of-the-art methods, demonstrating its advantage in ship detection.