中图分类号：TM71 文献标志码：A DOI: 10.16157/j.issn.0258-7998.234109 中文引用格式： 刘禹泽，潘明明，邹华，等. 基于图像识别的用电安全检查子系统设计与实现[J]. 电子技术应用，2023，49(10)：23-28. 英文引用格式： Liu Yuze，Pan Mingming，Zou Hua，et al. Design and implementation of electricity safety inspection subsystem based on monitoring image data[J]. Application of Electronic Technique，2023，49(10)：23-28.
Design and implementation of electricity safety inspection subsystem based on monitoring image data
Liu Yuze1，Pan Mingming2，Zou Hua1，Wang Baigen3，Wang Ou3，Zhao Qian4，Liu Huizhou4
(1.State Key Laboratory of Networking and Switching Technology， Beijing University of Posts and Telecommunications， Beijing 100876， China；2.China Electric Power Research Institute， Beijing 100192， China； 3.Anqing Power Supply Company of State Grid Anhui Electric Power Co.， Ltd.， Anqing 246000， China； 4.State Grid Anhui Electric Power Co.， Ltd.， Hefei 230061， China)
Abstract： Electricity safety inspection is an important way to ensure the normal operation of the power grid. Traditional electricity safety inspection mainly relies on manual inspection of places and equipment with safety hazards one by one. With the development of artificial intelligence technology, intelligent analysis based on image data can assist in timely identification of relevant safety hazards, reduce the experience requirements for inspectors, and improve efficiency while ensuring the accuracy of safety inspections. In order to better improve the accuracy of electricity safety inspection, the article proposes an electricity hazard identification algorithm based on YOLO neural network, which can dynamically identify the indicator lights of electrical equipment and compare them with normal states, and promptly issue alarm messages when abnormal states are found. Based on this algorithm, the article also designed and implemented an electricity safety inspection subsystem based on image recognition. Through actual data validation, the system can achieve a high level of inconsistent detection of equipment indicator status, meeting the demand for electricity safety inspection.
Key words : target detection algorithm；electricity safety inspection sub system；image recognition