Driver fatigue state detection based on improved YOLOv5
In order to reduce the sudden traffic accidents caused by fatigue driving,a network model based on improved YOLOv5 was proposed to detect the fatigue state of drivers.Firstly,the original YOLOv5 backbone network was replaced by the lightweight network MobileNetV3.Secondly,the ECA attention mechanism was incorporated into each C3 module of the neck network.Finally,the degree of eyes opening and closing and mouth with or without snorting were located and identified by the detection network,and then the multiple indexes were used to judge the fatigue state of the driver,and the self-built fatigue detection dataset was used for experiments.The results show that the improved YOLOv5 model's number of parameters,computations,and volume are reduced to 48%,38%,50%of the original model,respectively,which solves the problem of excessive number of parameters,computations and volume of the original model.The mAP value is increased from 98.6%to 99.1%,the accuracy is increased from 95.9%to 96.8%,and the detection rate is increased from 115 f/s to 119 f/s,all of which further improves the detection accuracy and speed of the mode.The improved YOLOv5 model has the characteristics of lightweight,high precision and high speed,which can provide reference for fatigue driving early warning.
万方数据
维普
