基于深度学习的疲劳驾驶检测算法

Fatigue Driving Detection Algorithm Based on Deep Learning

针对现有疲劳驾驶检测算法实用性差或准确率低的问题,本文提出了一种基于深度学习的疲劳驾驶检测算法.首先,使用HOG(Histogram of Oriented Gradient)特征算子检测人脸的存在;其次,利用特征点模型实现人脸的对齐,同时实现眼睛、嘴巴区域的分割;最后通过深度卷积神经网络提取驾驶员的眼部疲劳特征,并融合驾驶员嘴部的疲劳特征进行疲劳预警.大量的实验表明,该方法在疲劳驾驶检测的准确率、实时性等方面都取得明显的性能提升.

A fatigue driving detection algorithm based on deep learning was proposed to solve the problem of poor practicability or low accuracy of existing fatigue driving detection algorithms.First,the Histogram of Oriented Gradient（HOG）feature operator was used to detect the presence of human faces.Secondly,the feature points model was used to realize the face alignment and segment the eye and mouth regions.Finally,the fatigue features of the driver＇s eyes were extracted by the deep convolutional neural network,and the fatigue features of the driver＇s mouth were fused to carry out fatigue warning.Experiments show that the proposed method can achieve significant performance improvement in terms of accuracy and real-time performance of fatigue driving detection.