Open Access

ARTICLE

Special Vehicle Target Detection and Tracking Based on Virtual Simulation Environment and YOLOv5-Block+DeepSort Algorithm

by Mingyuan Zhai^1,2, Hanquan Zhang¹, Le Wang¹, Dong Xiao^1,*, Zhengmin Gu³, Zhenni Li¹

1 College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
2 Flight Control Department, Shenyang Aircraft Design and Research Institute, Shenyang, 110035, China
3 Department of Information Center, The First Hospital of China Medical University, Shenyang, 110001, China

* Corresponding Author: Dong Xiao. Email:

Computers, Materials & Continua 2024, 81(2), 3241-3260. https://doi.org/10.32604/cmc.2024.056241

Received 17 July 2024; Accepted 24 September 2024; Issue published 18 November 2024

Abstract

In the process of dense vehicles traveling fast, there will be mutual occlusion between vehicles, which will lead to the problem of deterioration of the tracking effect of different vehicles, so this paper proposes a research method of virtual simulation video vehicle target tracking based on you only look once (YOLO)v5s and deep simple online and realtime tracking (DeepSort). Given that the DeepSort algorithm is currently the most effective tracking method, this paper merges the YOLOv5 algorithm with the DeepSort algorithm. Then it adds the efficient channel attention networks (ECA-Net) focusing mechanism at the back for the cross-stage partial bottleneck with 3 convolutions (C3) modules about the YOLOv5 backbone network and before the up-sampling of the Neck feature pyramid. The YOLOv5 algorithm adopts expected intersection over union (EIOU) instead of complete intersection over union (CIOU) as the loss function of the target frame regression. The improved YOLOv5 algorithm is named YOLOv5-Block. The experimental results show that in the special vehicle target detection (TD) and tracking in the virtual simulation environment, The YOLOv5-Block algorithm has an average accuracy (AP) of 99.5%, which significantly improves the target recognition correctness for typical occlusion cases, and is 1.48 times better than the baseline algorithm. After the virtual simulation video sequence test, multiple objects tracking accuracy (MOTA) and various objects tracking precision (MOTP) improved by 10.7 and 1.75 percentage points, respectively, and the number of vehicle target identity document (ID) switches decreased. Compared with recent mainstream vehicle detection and tracking models, the YOLOv5-Block+Deepsort algorithm can accurately and continuously complete the detection and tracking tasks of special vehicle targets in different scenes.

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Keywords

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