基于深度学习的疲劳驾驶检测算法

doi:10.19678/j.issn.1000-3428.0055912

摘要/Abstract

摘要： 为实现复杂驾驶环境下驾驶人员疲劳状态识别与预警，提出基于深度学习的疲劳驾驶检测算法。利用基于shuffle-channel思想的MTCNN模型检测常规摄像头实时采集的驾驶人员人脸图像，使用PFLD深度学习模型进行人脸关键点检测以定位眼部、嘴部和头部位置，从中提取眨眼频率、嘴巴张开程度和点头频率等特征参数，并通过多特征融合策略获取驾驶人员疲劳状态，从而实现疲劳驾驶的有效预警。实验结果表明，该算法给出的疲劳驾驶预警结果均未出现误判情况，具有较高的检测准确率和较好的鲁棒性。

关键词: 疲劳驾驶检测, 疲劳特征提取, PERCLOS值, 人脸检测, 人脸关键点检测, 头部姿态估计

Abstract: To realize identification and warning of fatigue driving detection in complex driving environment,this paper proposes an algorithm for fatigue driving detection based on deep learning. The algorithm uses the MTCNN model based on the shuffle-channel concept to detect the facial images of drivers collected in real time by normal cameras.Then the PFLD deep learning model is used for facial keypoint detection to locate the eyes,the mouth and the head,so as to extract the feature parameters including the blinking rate,the extent to which the mouth opens,and the nodding frequency.Finally,based on the multi-feature fusion strategy,the fatigue state of the driver is obtained to implement effective alarming for fatigue driving.Experimental results show that false warning do not occur in fatigue driving warning generated by the proposed algorithm,which means the proposed algorithm has a high detection accuracy and robustness.

Key words: fatigue driving detection, fatigue feature extraction, PERCLOS value, face detection, face keypoint detection, head pose estimation

中图分类号:

TP391

郑伟成, 李学伟, 刘宏哲, 代松银. 基于深度学习的疲劳驾驶检测算法[J]. 计算机工程, 2020, 46(7): 21-29.

ZHENG Weicheng, LI Xuewei, LIU Hongzhe, DAI Songyin. Fatigue Driving Detection Algorithm Based on Deep Learning[J]. Computer Engineering, 2020, 46(7): 21-29.

https://www.ecice06.com/CN/Y2020/V46/I7/21

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