现代信息科技2024，Vol.8Issue(22)：51-55,5.DOI:10.19850/j.cnki.2096-4706.2024.22.011
基于YOLOv8算法的电厂场景安全帽佩戴检测识别方法
Detection and Recognition Method of Safety Helmet Wearing in Power Plant Scene Based on YOLOv8 Algorithm
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作者信息
- 1. 国能(肇庆)热电有限公司,广东 肇庆 526238
- 折叠
摘要
Abstract
Safety helmet wearing is crucial to the safety of power plant construction personnel,but the construction personnel inevitably fall off the helmet in the complex power plant environment.In order to judge whether the construction personnel wear safety helmets,this paper proposes a safety helmet detection and recognition method based on the YOLOv8 power plant scene.Aiming at the problem of insufficient number of samples in the power plant scene of the open source safety helmet data set,the power plant scene data is collected,cleaned and labeled,and the safety helmet data set is reconstructed.Based on the ultralytics framework,the YOLOv8 Nano neural network model is used to train the data set,and a network model with FPS of 91.7 and AP50 of 93.5%is obtained.The experimental results show that this method can effectively and quickly detect whether the construction personnel wear the safety helmet,and has a good application effect.关键词
安全帽检测/安全帽识别/电厂施工场景/深度学习/YOLOv8Key words
safety helmet detection/safety helmet recognition/power plant construction scene/Deep Learning/YOLOv8分类
信息技术与安全科学引用本文复制引用
文显华..基于YOLOv8算法的电厂场景安全帽佩戴检测识别方法[J].现代信息科技,2024,8(22):51-55,5.
