A multi-target tracking algorithm for water naviga-tion based on the fusion of YOLOv5 and DeepSORT was pro-posed to address the problem of significant differences between tracking target frames in low frame rates or missing frames in video images for targets such as cruising ships or submarines sailing near the water surface,resulting in decreased tracking accuracy and efficiency.Firstly,a super-resolution recon-struction network was introduced to enhance the tracking tar-get image to eliminate the interference of clouds,fog,and waves on the recognition network and make the target features in the image clear.Secondly,the ShuffleAttention module was introduced in YOLOv5 to enhance the recognition network's ability to extract target features.Finally,in the DeepSORT al-gorithm cascade matching,Euclidean distance matching was introduced instead of IOU matching to improve target tracking accuracy.Simulation results show that the tracking perform-ance of the algorithm proposed is good,and the improved YOLOv5 model has increased the mAP50-95 value by 9.4％,and in the DeepSORT tracking network,the tracking accuracy has increased by 8.11％compared to before optimization.
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