Detection Method of Key Ship Parts Based on YOLOv11

The current ship target detection algorithm is not adaptable to changes in target size and position, and its detection accuracy and speed are difficult to balance in the process of algorithm improvement. In particular, there is little research on the detection of key parts of ships. In this paper, an improved YOLOv11 ship key location detection algorithm is proposed. It is difficult to balance the relationship between detection efficiency and model complexity using current key parts detection algorithms. The processing performance of deforming key parts according to the algorithms is not strong. Moreover, the algorithms’ detection performance of occluding overlapping targets and small-size targets is not high. In view of the practical problems existing in the algorithms and the realistic demand for real-time and accurate detection of key parts of targets on dynamic ships at sea, three aspects of improvement on YOLOv11 are proposed. The visible light ship key position dataset is then constructed for the experiment. The experimental results show that the improved YOLOv11 network in this paper can detect the key position information of ships more accurately and efficiently, effectively balance the relationship between model accuracy and speed, overcome the interference of dynamic factors, improve the fine interpretation of dynamic ship information at sea, and provide a feasible technical approach for accurately and precisely mastering ship information at sea and realizing real-time sea area detection.