基于眼口状态的疲劳检测系统

Driver fatigue detection system based on states of eyes and mouth

为了快速、准确地监测驾驶员的疲劳状态,提出了一种基于眼口状态的疲劳检测算法。通过一个多任务级联神经网络(MTCNN)对驾驶员进行快速人脸检测以及人脸特征点定位;通过卷积神经网络(CNN)分别对定位后的眼口区域进行人眼睁闭和嘴巴张闭状态预测;通过计算眼睑闭合率(PERCLOS)参数和驾驶员的嘴部动作频率判断驾驶员的疲劳状态。实验表明:提出的方法在保持较高的检测准确率的同时,能够快速对驾驶员的疲劳状态进行检测,达到实时性的要求。

A driver fatigue detection algorithm which is based on the states of eyes and mouth of drivers is proposed to detect fatigue state. A multitask cascaded convolutional networks（ MTCNN） is applied for face detection and feature point positioning. After positioning,another convolution neural network（ CNN） is used for eye and mouth regions state recognition. By calculating the percentage of eyelid closure（ PERCLOS） parameter and the frequency of mouth movements,determine fatigue state of driver. The experimental results show that the proposed method has high detecting accuracy and can quickly detect fatigue state of driver and meet requirements of real-time.