This research develops a YOLOv8-based model that improves ship detection accuracy in complex sea conditions, suggesting better maritime traffic management.
With the rapid development of the marine economy, maritime traffic supervision and safety management have raised higher requirements for ship detection. Traditional methods suffer from low efficiency and high false detection rates, making it difficult to meet the needs of intelligent maritime management. This paper proposes a ship target detection model based on YOLOv8. Experimental results show that the model achieves 99.15% mAP@50 and 85.14% mAP@50:95, effectively handling ship recognition tasks under complex sea conditions, providing reliable technical support for smart ocean construction and maritime supervision.
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Wanqiu Xu (2025) studied this question.
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