Open AccessResearch on Driver Fatigue State Detection Method Based on Deep Learning
Author(s) -
Yonghui Wang,
Roufei Qu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1744/4/042242
Subject(s) - robustness (evolution) , closing (real estate) , computer science , artificial intelligence , deep learning , fault detection and isolation , real time computing , pattern recognition (psychology) , computer vision , biochemistry , chemistry , political science , law , actuator , gene
Fatigue driving detection is essential to ensure the safety of society and drivers. At present, most fatigue detection methods are relatively traditional and single, and have complex algorithms, low accuracy, and low fault tolerance. Based on the improved Multi-task Cascaded Convolutional Network (MTCNN) to achieve precise positioning of facial feature points, combined with the Res-SE-net model to achieve eye, mouth area and state classification. The model is trained, and finally the driver fatigue is judged based on the PERCLOS rule combined with the OMR rule of mouth opening and closing frequency. Experimental results show that this method can effectively extract fatigue features, has high detection accuracy, meets real-time requirements, and has high robustness to complex environments.