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Image sequence-based risk behavior detection of power operation inspection personnel

2023-01-13

【论文推荐】中国电科院蔡常雨等：基于图像序列的电力运检人员安全风险行为检测技术

原创英文期刊编辑部全球能源互联网期刊 2023-01-13 08:00

摘要

为实现对电力运检人员安全风险行为的高精度检测，本文提出了一种基于图像序列的安全风险行为检测方法。该方法首先将原始图像序列数据分离成前景图像和背景图像。然后，在前景图像中，使用自由锚框检测方法检测人员并纠正其方向。最后提取人体姿态节点，利用每帧图像序列中提取的人体姿态节点来识别人的异常行为。仿真实验表明，该算法在人体姿态节点检测和风险行为识别的准确性方面具有显着优势。

Image sequence-based risk behavior detection of power operation inspection personnel

基于图像序列的电力运检人员安全风险行为检测技术

Changyu Cai1, Jianglong Nie2, Wenhao Mo1, Zhouqiang He2, Yuanpeng Tan1, Zhao Chen2

(1.Artificial Intelligence Application Department, China Electric Power Research Institute, Beijing 100192, P.R. China

2.State Grid Gansu Electric Power Company, Lanzhou 730070, P. R. China)

收听作者1分钟语音介绍

Image sequence-based risk behavior detection of power opera

Abstract

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper. In this method, the original image sequence data is first separated from the foreground and background. Then, the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction. Finally, human posture nodes are extracted from each frame of the image sequence, which are then used to identify the abnormal behavior of the human. Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.

Keywords

Human posture node detection, Risk behavior detection, Image sequence, Anchor-free detection, Power maintenance personnel.

Fig.1 Diagram of image sequence-based risk behavior detection of power maintenance personnel

Fig.2 Diagram of foreground-background separation

Fig.3 Diagram of anchor-free human body detection

Fig.4 Diagram of human posture node detection

Fig.5 GNN model structure diagram (not all links between nodes in S are shown)

Fig.6 Foreground-background separation

Fig.7 Comparison of human posture node detection withoutinclination angle

Fig.8 Comparison of human posture node detection with inclination angle

Fig.9 PCKh index comparison of human posture node detection considering foreground and background information

Fig.10 PCKh index comparison of human posture node

detection considering inclination angle

本文引文信息

Cai CY, Nie JL, Mo WH, et al. (2022) Image sequence-based risk behavior detection of power operation inspection personnel. Global Energy Interconnection, 5(6): 604-617

蔡常雨，聂江龙，莫文昊等 (2022) 基于图像序列的电力运检人员安全风险行为检测技术. 全球能源互联网（英文）, 5(6): 604-617

Biographies

Changyu Cai

Changyu Cai was born in Hebei, China, in 1983. He received his Master’s degree from Changchun University of Science and Technology, Changchun, China, in 2010. He is working at China Electric Power Research Institute, Beijing, China. His research interests include electric power, artificial intelligence, and computer vision and applications.

Jianglong Nie

Jianglong Nie was born in China in 1973. He is working at State Grid Gansu Electric Power Company, Lanzhou, China. His research interests include electric power, artificial intelligence, and electrical engineering.

Wenhao Mo

Wenhao Mo was born in Shijiazhuang, China in 1996. He received his Master’s degree in Control science and engineering from Harbin Institute of Technology in 2020. Since 2020, he has been working as an engineer in Artificial intelligence application department, China Electric Power Research Institute (CEPRI). His research areas include power equipment inspection, deep learning, and image processing.

Zhouqiang He

Zhouqiang He was born in China in 1981. He is working at State Grid Gansu Electric Power Company, Lanzhou, China. His research interests include electric power, artificial intelligence, and electrical engineering.

Yuanpeng Tan

Yuanpeng Tan was born in Tangshan, China in 1987. He received his Ph.D. degree in Power information technology from North China Electric Power University in 2017. Currently, he is working as a senior engineer in the Artificial Intelligence Application Department, China Electric Power Research Institute (CEPRI). His research interests include power equipment inspection, knowledge graph, graph computing, and other technologies.

Zhao Chen

Zhao Chen was born in China in 1988. He is currently working at State Grid Gansu Electric Power Company, Lanzhou, China. His research interests include electric power, artificial intelligence, and electrical engineering.

编辑：王彦博

审核：王伟

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