基于YOLOv8的车辆驾驶员疲劳检测应用研究

Application of Vehicle Driver Fatigue DetectionBased on YOLOv8

疲劳驾驶是导致交通事故发生的主要原因,由于疲劳驾驶检测场景的复杂性和实时性要求,提出了一种基于YOLOv8的车辆驾驶员疲劳检测预警设计方法,对YOLOv8算法在注意力机制、数据增强、轻量化网络等方面进行改进,提高车辆驾驶员疲劳检测的识别精度和检测速率,同时提取人脸关键点计算眼睛纵横比(EAR),建立疲劳度评价分类模型,实现对疲劳驾驶的综合判断和预警。搭建车辆驾驶员疲劳检测实验平台,并进行验证。结果表明,该方法可以准确获取疲劳检测结果,准确率达到94%。

Fatigue driving is the main cause of traffic accidents.Due to the complexity and real-time requirements of fatigue driving detection scenarios,a vehicle driver fatigue detection and warning design method based on YOLOv8 is proposed.The YOLOv8 algorithm is improved in attention mechanism,data augmentation,lightweight network,etc.to improve the recognition accuracy and detection rate of vehicle driver fatigue detection.Mean while,key facial points are extracted to calculate the eye aspect ratio(EAR),and a fatigue evaluation classification model is established to achieve comprehensive judgment and warning of fatigue driving.A vehicle driver fatigue detection experimental platform is built to verify it.The results show that this approach can accurately obtain fatigue detection results,with an accuracy rate of 94%.