基于YOLOv7的智能电网外部安全帽佩戴风险因素识别与检测  

Identification and detection of external risk factors for safety helmet wearing in smart grid based on YOLOv7

作　　者：刘甜甜 彭放 卢伟龙 潘建宏 张婉 LIU Tiantian;PENG Fang;LU Weilong;PAN Jianhong;ZHANG Wan(Big Data Center,State Grid Corporation of China,Beijing 100000,China;Fujian Yirong Information Technology Co.,Ltd.,Fuzhou 350003,China;State Grid Jilin Electric Power Co.,Ltd.,Changchun 130022,China)

机构地区：[1]国家电网有限公司大数据中心,北京100000 [2]福建亿榕信息技术有限公司,福州350003 [3]国网吉林省电力有限公司,长春130022

出　　处：《电测与仪表》2024年第12期42-48,共7页Electrical Measurement & Instrumentation

基　　金：国家电网有限公司科技项目(SGSJ0000FXJS2100093)。

摘　　要：在电网施工作业过程中,安全帽的正确佩戴对于保护作业人员的人身健康、保证作业项目的顺利进行甚至电网的安全运行具有重要意义。针对智能电网施工作业过程中作业人员未正确佩戴安全帽带来的外部风险因素问题,基于YOLOv7目标检测模型设计了一种作业人员安全帽佩戴在线检测识别系统。文章在智能电网安全帽佩戴在线检测系统架构的基础上分析了YOLOv7模型的结构及其应用,进而基于改进的数据集对所述方法的性能和效果进行了分析验证。实验结果表明,相比于前代模型,YOLOv7具有更精确的检出率及更快的检测速度,能够更好地满足智能电网作业人员安全帽佩戴外部风险因素的实时检测需求。In the process of power grid construction,safety helmet is of great significance for protecting the personal health,ensuring the smooth operation of project and even the safety of power grid.Considering the external risk factors caused by incorrect wearing of safety helmets of operators,we design an online detection and identification system for safety helmets wearing based on YOLOv7 model.We first analyze the structure and application of YOLOv7 model based on the online detection system architecture of smart grid helmet wearing,and then,analyze and verify the performance and effect of the proposed method using an improved data set.The experiment results show that compared with the previous generation models,YOLOv7 has a more accurate detection rate and faster detection speed,which can better meet the real-time detection requirements of external risk factors for safety helmets wearing in smart grid.

关 键 词：智能电网 风险因素 识别 检测 YOLOv7 

分 类 号：TM93[电气工程—电力电子与电力传动]