Ships target detection plays a crucial role in safeguarding maritime transportation. However, affected by factors such as ocean waves, extreme weather, and target diversity (e.g., large size differences, arbitrary rotation, and occlusion), existing deep learning-based detection methods struggle to achieve a satisfactory balance among accuracy, speed, and model size in complex marine environments. To address this challenge, this paper proposes a real-time ship detection algorithm (C-YOLO) integrating global perception and multi-scale feature enhancement. First, a Transformer encoder is added before the detection head, which suppresses interference from sea clutter and cloud mist occlusion through long-range dependency modeling, improving the detection of small and occluded ships. Second, a Dual-Effect Focused Residual Fusion Module is designed to replace the backbone’s multi-scale pooling structure, combining the advantages of CBAM (background noise suppression) and SK-Net (dynamic scale adaptation) to simultaneously capture features of ships of different sizes. Finally, a CZIoU loss function is proposed, which integrates constraints on angle, center point, vertex, and area to address rotation, deformation, and multi-scale issues in ship detection. Experimental results on the SeaShips 7000 dataset show that the proposed C-YOLO achieves a Recall of 0.842, mAP@50 of 0.797, and mAP@50:95 of 0.552, outperforming mainstream algorithms such as YOLOv7 (Recall = 0.785, mAP@50 = 0.781), YOLOv9s (Recall = 0.819, mAP@50 = 0.755), and SSD (Recall = 0.802, mAP@50 = 0.833). With 76.75 M parameters and an inference speed of 119 FPS, the model maintains efficient real-time performance while ensuring detection accuracy. This method effectively reduces false detection and missed detection rates in complex scenarios such as port monitoring and maritime traffic control, providing a reliable technical solution for intelligent maritime surveillance and safe navigation—with significant practical value for improving maritime transportation efficiency and reducing safety risks.
Cai et al. (2026) studied this question.