首页|基于YOLOv3的驾驶员疲劳检测系统的算法设计

基于YOLOv3的驾驶员疲劳检测系统的算法设计

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该算法设计利用高效的YOLOv3对实时驾驶员疲劳监测系统进行了研究并构建.YOLOv3算法以其快速且精准的物体识别能力,为目标检测领域带来了颠覆性的变革.该系统融合了先进的面部关键点定位技术,可精确捕捉眼部动作,通过监测眨眼频率等指标,对驾驶者的疲劳程度进行精准评估.实验部分在云端平台展开,借助大量数据对模型进行训练,不仅证明了系统的实时响应性和实用价值,更为智能化的提升道路交通安全防范提供了新的思路.
Algorithm design of driver fatigue detection system based on YOLOv3
The algorithm design utilizes the efficient YOLOv3 to investigate and construct a real-time driver fatigue monitoring system.The YOLOv3 algorithm,with its fast and accurate object recognition capability,has brought a subversive change to the field of object detection.The system incorporates advanced facial key point localization technology,which can accurately capture eye movements and accurately assess the driver's fatigue level by monitoring indicators such as blink rate.The experimental part is car-ried out on the cloud platform,and the model is trained with the help of a large amount of data,which not only proves the real-time responsiveness and practical value of the system,but also provides a new idea for more intelligent road traffic safety prevention.

YOLOv3driver fatigue detectiondeep learningtarget detection

张志勇、肖波、韩涛、陈怡帆、吴永哲、程莹

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湖北师范大学电气工程与自动化学院,黄石 435002

YOLOv3 驾驶员疲劳检测 深度学习 目标检测

2024

现代计算机
中大控股

现代计算机

影响因子:0.292
ISSN:1007-1423
年,卷(期):2024.30(24)