Research on detection and tracking methods of unmanned ship water targets based on light vision

This study explores technical methods based on light vision to address the problem of target detection and tracking by surface unmanned ships in complex environments. We utilize an improved dark channel dehazing method and guided filtering for image preprocessing to improve the accuracy and efficiency of subsequent image processing. In terms of target detection, the YOLOv7 algorithm is used, which effectively improves the accuracy and recall rate of target detection by optimizing the loss function. In order to achieve accurate multi-target tracking, combined with self-trained model weights and Sort algorithm, continuous tracking of targets and accurate annotation of center point trajectories are successfully implemented. In addition, a binocular camera system is built on an unmanned ship platform for target ranging. Experimental results show that our method can achieve the ranging function with an average relative error of 6.46%. This result not only improves the navigation and positioning capabilities of unmanned ships, but also provides technical support for water surface safety monitoring. This research demonstrates that in the field of surface unmanned ships, target detection and tracking problems can be effectively solved by integrating advanced image processing technology and machine learning algorithms.