基于机器学习和图像识别的电力作业现场安全监督方法

doi:10.11930/j.issn.1004-9649.201811057

中国电力 ›› 2020, Vol. 53 ›› Issue (4): 155-160.DOI: 10.11930/j.issn.1004-9649.201811057

基于机器学习和图像识别的电力作业现场安全监督方法

常政威¹, 彭倩¹, 陈缨²

1. 国网四川省电力公司电力科学研究院，四川成都 610094;
2. 国网四川综合能源服务有限公司，四川成都 610072

收稿日期:2018-11-16 修回日期:2019-10-01 发布日期:2020-04-05
作者简介:常政威(1981-),男,通信作者,高级工程师(教授级),博士,从事图像处理与图像识别研究,E-mail:changzw@126.com;彭倩(1983-),男,高级工程师,硕士,从事电力作业现场安全管控研究,E-mail:pq8324@163.com;陈缨(1967-),男,高级工程师(教授级),硕士,从事电力系统自动化研究,E-mail:262455988@qq.com
基金资助:
四川省重点研发项目（自主导航的智能服务机器人研制及产业化，2017GZ0068）；国网四川省电力公司科技项目（自主导航智能机器人研制及电力作业现场安全管控中的应用研究，52199716002P）

Safety Supervision Method for Power Operation Site Based on Machine Learning and Image Recognition

CHANG Zhengwei¹, PENG Qian¹, CHEN Ying²

1. State Grid Sichuan Electric Power Company Electric Power Research Institute, Chengdu 610094, China;
2. State Grid Sichuan Comprehensive Energy Service Corporation, Chengdu 610072, China

Received:2018-11-16 Revised:2019-10-01 Published:2020-04-05
Supported by:
This work is supported by Key Research and Development Projects in Sichuan Province (Development and Industrialization of Intelligent Navigation Robots for Autonomous Navigation, No.2017GZ0068) and State Grid Sichuan Electric Power Company Science and Technology Project (Application Research on Autonomous Navigation Intelligent Robot Development and Power Operation Site Safety Control, No.52199716002P)

摘要/Abstract

摘要： 针对电力作业现场人员误入危险区域的安全问题，开展人员闯入检测的研究，首先利用梯度方向直方图（histogram of oriented gradient，HOG）和支持向量机（support vector machine，SVM）进行完全帧的人员检测，然后利用基于OpenCV的图像处理技术判断人员是否闯入警戒区域。通过视频监控设备采集作业现场图像，采用上述方法实时识别现场人员及其危险行为，并发出告警信号。实验结果表明，检测结果准确率达到92%，实现了电力作业现场安全监督自动化，显著提升了作业现场安全水平。

关键词: 梯度方向直方图, 支持向量机, 人员闯入检测, 电力作业现场, 安全监督

Abstract: Aiming at the safety supervision of power operation site, a research is carried out on personnel intrusion detection. It is proposed to use the histogram of oriented gradient (HOG) and support vector machine (SVM) for full frame personnel detection, and then the OpenCV-based image processing technology is used to determine any personnel intrusion into the alert area. Through the images of the work site captured by video monitoring equipment, the above-said methods are used to identify the on-site personnel and their dangerous behaviors in real time, and to issue an alarm signal. The experimental results show that the accuracy of the detecting results reaches 92%, and the automatic safety supervision of power operation site is realized, which greatly improves the safety level of the power operation site.

Key words: histogram of oriented gradient, support vector machine, intrusion detection, power operation site, safety supervision

常政威, 彭倩, 陈缨. 基于机器学习和图像识别的电力作业现场安全监督方法[J]. 中国电力, 2020, 53(4): 155-160.

CHANG Zhengwei, PENG Qian, CHEN Ying. Safety Supervision Method for Power Operation Site Based on Machine Learning and Image Recognition[J]. Electric Power, 2020, 53(4): 155-160.

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https://www.electricpower.com.cn/CN/Y2020/V53/I4/155

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基于机器学习和图像识别的电力作业现场安全监督方法

Safety Supervision Method for Power Operation Site Based on Machine Learning and Image Recognition

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基于机器学习和图像识别的电力作业现场安全监督方法

Safety Supervision Method for Power Operation Site Based on Machine Learning and Image Recognition

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