兵工自动化2024，Vol.43Issue(5)：33-36,42,5.DOI:10.7690/bgzdh.2024.05.007
基于改进YOLO的不规范佩戴安全帽检测
Detection of Nonstandard Wearing of Safety Helmet Based on Improved YOLO
郭威 1樊彦国 1栗晓政 1张兴富 2王满意2
作者信息
- 1. 国网河南省电力公司,郑州 450018
- 2. 北京中电普华信息技术有限公司,北京 100089
- 折叠
摘要
Abstract
In order to solve the problem of low efficiency and accuracy in the detection of non-standard safety helmet worn by the existing substation patrol personnel,a lightweight substation personnel non-standard behavior detection model based on improved YOLO is proposed.The model consists of a feature extraction network,an ECA-SPP network,an ECA-PANet network and a prediction network;MobileNet V3 is used in the feature extraction network;feature maps of four scales are extracted and input into the SPP and PANet networks,and are optimized based on an attention mechanism;The effectiveness of the proposed model is verified by the data set of the detection of non-standard wearing of safety helmets in substations.The experiment results show that the proposed model mAP is a 0.8244 and FPS is a 38.06,which is obviously better than other models such as Faster RCNN,YOLOv4 and YOLOx,and has higher accuracy and faster detection speed.It can provide a reference for real-time detection of substation personnel wearing non-standard safety helmet.关键词
电力系统/异常检测/负荷预测/支持向量机Key words
power system/anomaly detection/load forecasting/support vector machine分类
信息技术与安全科学引用本文复制引用
郭威,樊彦国,栗晓政,张兴富,王满意..基于改进YOLO的不规范佩戴安全帽检测[J].兵工自动化,2024,43(5):33-36,42,5.
