Research on Automatic Navigational Buoy Recognition Based on YOLOv11

With the rapid development of intelligent ships, deep learning-based automatic identification of maritime buoys has emerged as a critical research direction for maritime intelligence. To address the challenge of balancing real-time performance and detection accuracy in complex inland waterway environments, this paper proposes an automatic identification method based on the YOLOv11 object detection algorithm. Specifically, by integrating advanced C3K2 and C2PSA modules, the model's capability for feature extraction and global information perception in cluttered backgrounds is significantly enhanced. To mitigate the scarcity of data samples, data augmentation techniques-including rotation and Gaussian noise elimination-were applied to construct buoy dataset, which consists of 913 high-quality annotated images. Furthermore, an incremental learning strategy with multi-stage iterative training was introduced to improve the model's generalization across diverse scenarios. Experimental results demonstrate that while maintaining high-efficiency real-time response, the proposed model achieves a mAP of 93%. This performance outperforms traditional algorithms such as Cascade-RCNN and SSD, as well as previous versions like YOLOv8, providing robust technical support for safe collision avoidance and waterway situational awareness in intelligent shipping.