疲劳驾驶是导致车祸的重要诱因, 严重危害道路交通安全, 而车辆行驶过程中的光照条件变化、驾驶员姿态调整和眼镜遮挡等因素将对疲劳检测任务产生不利的影响。针对以上问题, 提出了基于深度学习的驾驶员疲劳检测算法。首先, 使用850 nm红外光源补光, 在复杂光照和遮挡形态下采集驾驶员的面部图像; 其次, 利用红外图像中的多种特征, 通过级联CNN确定人脸边框和特征点位置, 提取眼睛区域并识别眼睛的睁闭状态; 最后, 将眼睛状态识别结果和连续图像中的特征点坐标差值输出至LSTM网络, 检测驾驶员疲劳状态。实验结果表明: 该疲劳检测算法的准确率可达94.48%, 平均检测时间为65.64 ms。

Fatigue driving is the main cause or reason for traffic accidents, which has a huge influence on social safety. Considering the fact that light change and glasses could significantly increase the difficulty to monitor human eyes, fatigue detection was still an unsolved problem. A new driver fatigue method based on morphology infrared features and deep learning were proposed. Using 850 nm infrared light source, the facial image was obtained. Human faces and landmarks which indicated the area of eyes were located by Convolution Neural Network(CNN) with morphology features in infrared image. In the next step, a filter module which measured head displacement was added, aiming at reducing the impact of posture change. In the following, the collected facial states were transformed into sequential data. Finally, the sequential data was passed to the Long Short Term Memory(LSTM) network to detect fatigue state by analyzing the sequential correlations. Experimental results show that the accuracy of the fatigue detection algorithm can reach 94.48% with an average detection time of 65.64 ms.