Research on fatigue detection based on visual features

作     者：Zhao, Guangzhe He, Yanqing Yang, Hanting Tao, Yong 

作者机构：Beijing Univ Civil Engn & Architecture Coll Elect & Informat Engn Beijing 102600 Peoples R China Coll Beihang Univ Beijing Peoples R China 

出 版 物：《IET IMAGE PROCESSING》 (IET影像处理)

年 卷 期：2022年第16卷第4期

页      面：1044-1053页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Pyramid Talent Training Project of BUCEA 

主　　题：VGG16 network convolutional neural network visual features fatigue detection method road safety advanced driver assistance system convolutional neural nets fatigue features road accidents face detection part fatigue monitoring learning (artificial intelligence) automakers modified data sets Training test feature extraction multiphysical feature fusion detection method decision making Single-Shot MultiBox Detector algorithm NTHU-Drowsy Driver Detection data object detection fatigue driver information systems traffic engineering computing driver fatigue detection 

摘      要：The high incidence of traffic accidents brings immeasurable losses to life. In order to avoid such crises, researchers and automakers have used many methods to solve this problem. Among them, technology based on visual features is widely used in driver fatigue detection. As fatigue detection plays a vital role in the driving process, the high accuracy of fatigue monitoring is very important. This paper focuses on the method based on convolutional neural network to detect driver fatigue. First, in the face detection part, the Single-Shot Multi-Box Detector algorithm is used to improve the speed and accuracy of face detection to extract the eye and mouth regions;second, the VGG16 network is used to learn fatigue features, which is performed on the NTHU-Drowsy Driver Detection (NTHU-DDD) data set and the other two modified data sets Training test. The main result of this work is that the accuracy of fatigue monitoring is higher than other methods including the original method, with an accuracy rate of over 90%. And it has better generalization ability than the multi-physical feature fusion detection method. At the same time, we propose the fatigue detection method based on convolutional neural network to improve the advanced driver assistance system (ADAS) to make it more robust and reliable decision making.