摘要
为降低因驾驶人疲劳驾驶导致的交通事故,需要开展驾驶人疲劳检测研究。为满足在线实时检测的要求,本文提出了融合面部特征的机动车驾驶人疲劳检测方法,首先通过背景差分缩小检测区域、减少图像金字塔层数等方法对MTCNN人脸检测网络进行优化加速,加速后的速度与之前相比提升了258%。其次通过多级级联的残差回归树对人脸进行特征点检测,得到了人脸的特征点,最后通过融合面部嘴、眼开合度特征的方式建立驾驶人疲劳检测模型并进行训练。实验表明,该检测方法的准确率可达95.4%,每帧检测平均速度64 ms,检测速度快,能满足实时性的要求。
In order to reduce the traffic accidents caused by driver fatigue driving,it is necessary to carry out driver fatigue detection research.In order to meet the requirements of online real-time detection,a fatigue detection method for motor vehicle drivers based on facial features is proposed in this paper.Firstly,the MTCNN(multi-task cascaded convolutional networks)face detection network is optimized and accelerated by reducing the detection area through background difference,reducing the number of pyramid layers of image,etc.,and the speed after acceleration is 258%times faster than before.Secondly,the multi-level cascaded residual regression tree is used to detect the feature points of driver's face,and 68 feature points of the face are obtained.Finally,the driver fatigue detection model is established and trained by combining the features of face mouth and eye opening.The experimental results show that the accuracy of the proposed fatigue detection method can reach 95.4%,the average detection speed of each frame is 64ms,and the detection speed is fast enough for meeting the requirements of real-time.
作者
冯晓锋
方斌
FENG Xiaofeng;FANG Bin(Department of Traffic Management,Hunan Police Academy,Changsha 410138,China)
出处
《机械科学与技术》
CSCD
北大核心
2021年第11期1767-1772,共6页
Mechanical Science and Technology for Aerospace Engineering
基金
湖南省教育厅重点项目(20A173)
湖南省社会科学成果评审委员会项目(XSP19YBC053)。
关键词
背景差分
疲劳检测
面部特征
特征点检测
background difference
fatigue test
facial features
feature point detection
作者简介
冯晓锋(1980−),教授,博士,研究方向为机器视觉和道路交通安全,brucefxf@163.com。
