Multi-target intelligent tracking method for substation video surveillance based on YOLO v8 algorithm

LI Yiwei; LUO Lishi; ZHAO Bo; HE Hongliang; LI Weijiang; WU Jiansong; WANG Jinyuan; ZHOU Kexin

doi:10.11959/j.issn.1000-0801.2025025

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Multi-target intelligent tracking method for substation video surveillance based on YOLO v8 algorithm

Engineering and Application | 更新时间：2025-04-24

- Multi-target intelligent tracking method for substation video surveillance based on YOLO v8 algorithm
- Telecommunications Science Vol. 41, Issue 3, Pages: 179-189(2025)
- 作者机构：
  
  国网冀北电力有限公司超高压分公司，北京 102488
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2025025
  CLC： TN948.6
- Received：15 July 2024，
  
  Revised：2024-10-15，
  
  Published：20 March 2025
- 稿件说明：
移动端阅览
李奕炜,骆立实,赵波等.基于YOLO v8算法的变电站视频监控多目标智能跟踪方法[J].电信科学,2025,41(03):179-189.

LI Yiwei,LUO Lishi,ZHAO Bo,et al.Multi-target intelligent tracking method for substation video surveillance based on YOLO v8 algorithm[J].Telecommunications Science,2025,41(03):179-189.
李奕炜,骆立实,赵波等.基于YOLO v8算法的变电站视频监控多目标智能跟踪方法[J].电信科学,2025,41(03):179-189. DOI： 10.11959/j.issn.1000-0801.2025025.

LI Yiwei,LUO Lishi,ZHAO Bo,et al.Multi-target intelligent tracking method for substation video surveillance based on YOLO v8 algorithm[J].Telecommunications Science,2025,41(03):179-189. DOI： 10.11959/j.issn.1000-0801.2025025.

摘要

现阶段我国智能电网建设迅速发展，对变电站视频监控系统提出了更高的多样化、智能化需求。针对现有变电站视频监控摄像头对人员、车辆、异常入侵识别精度不足的问题，提出了一种基于YOLO v8算法的变电站视频监控多目标智能跟踪方法。首先，利用YOLO v8算法对变电站目标进行监测，通过采样算法加大感受野进而提升数据特征融合能力，采用注意力机制识别远处微小目标；然后，基于卡尔曼滤波器和变电站摄像头运动防抖，将YOLO v8监测到的信息输入BoTSORT 算法，完成多场景多目标下的变电站视频监控目标智能跟踪；最后，实验验证表明，该方法相比于YOLO v5、YOLO v7算法，目标识别平均精度均值分别提升了9.73个百分点、5.28个百分点，目标跟踪精度分别提升了12.34个百分点、8.41个百分点，提升了变电站视频监控系统智能化水平。

Abstract

With the rapid development of smart grid construction in China at present

there is a higher demand for diversified and intelligent video monitoring systems in substations. A multi-target intelligent tracking method for substation video surveillance based on YOLO v8 algorithm was proposed to address the issue of insufficient accuracy in identifying personnel

vehicles

and abnormal intrusions using existing substation video surveillance cameras. Firstly

the YOLO v8 algorithm was used to monitor the substation targets

and the sampling algorithm was used to increase the receptive field and improve the data feature fusion ability. The attention mechanism was used to identify small distant targets. Then

based on Kalman filter and substation camera motion stabilization

the information monitored by YOLO v8 was input into the BoTSORT algorithm to achieve intelligent tracking of substation video monitoring targets in multiple scenes and targets. Finally

experimental verification shows that compared to the YOLO v5 and YOLO v7 algorithms

the average accuracy of target recognition proposed has been improved by 9.73 percent and 5.28 percent

respectively

and the accuracy of target tracking has been improved by 12.34 percent and 8.41 percent

respectively. This has improved the intelligence level of the substation video monitoring system.

关键词

Keywords

references

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