Detection and Recognition Method of Safety Helmet Wearing in Power Plant Scene Based on YOLOv8 Algorithm
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.
safety helmet detectionsafety helmet recognitionpower plant construction sceneDeep LearningYOLOv8
国家科技期刊平台
NSTL
万方数据
