A multi-target tracking algorithm for low frame rate surface navigation based on YOLOv5-DeepSORT fusion

FU Shiwen, HAN Xingcheng, WANG Liming, WU Guoqiang, WANG Hongru, MA Wen, WANG Zhiyong

doi:10.16411/j.cnki.issn1006-7736.2024.01.011

Journal of Dalian Maritime University >

2024 , Vol. 50 >Issue 1: 94 - 101

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2024.01.011

A multi-target tracking algorithm for low frame rate surface navigation based on YOLOv5-DeepSORT fusion

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(1.School of Information and Communication Engineering，North University of China, Taiyuan 030051, China; 2. Taiyuan Heavy Industry Co., Ltd, Taiyuan 030024, China; 3.Shanxi Taizhong Shuzhi Technology Co., Ltd, Taiyuan 030024, China; 4. Shanxi Guohua Energy Co.,Ltd, Taiyuan 030000, China)

Received date: 2023-08-21

Revised date: 2023-10-18

Accepted date: 2023-10-18

Online published: 2023-08-21

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Abstract

A multi-target tracking algorithm for water navigation based on the fusion of YOLOv5 and DeepSORT was proposed 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 reconstruction network was introduced to enhance the tracking target 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 algorithm cascade matching, Euclidean distance matching was introduced instead of IOU matching to improve target tracking accuracy. Simulation results show that the tracking performance 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.

Key words： ship; surface navigation; multi-target tracking; super resolution reconstruction; YOLOv5; DeepSORT; Euclidean distance matching

Cite this article

FU Shiwen, HAN Xingcheng, WANG Liming, WU Guoqiang, WANG Hongru, MA Wen, WANG Zhiyong . A multi-target tracking algorithm for low frame rate surface navigation based on YOLOv5-DeepSORT fusion[J]. Journal of Dalian Maritime University, 2024 , 50(1) : 94 -101 . DOI: 10.16411/j.cnki.issn1006-7736.2024.01.011

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