DMS Fatigue Detection System For Driver In Vehicle

被引:0
|
作者
Mei ShiJie [1 ]
Pan Lian [1 ]
机构
[1] Wuhan Univ Sci & Technol, Sch Informat Sci & Engn, Wuhan, Peoples R China
关键词
MTCNN; fatigue driving detection; Deep learning; Safely belt detection; DMS;
D O I
10.1109/CCDC58219.2023.10327447
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Mining at the problem of automobile accidents caused by driver fatigue and driving distraction, a DAIS (Driving Monitoring System) driver monitoring system is designed to monitor driver status in real time. First, we detect the wearing of the driver's safety belt. We regard the safety belt detection process as a row based selection problem based on global features. The safety belt can be quickly detected by selecting rows. Then, face detection is carried out by MTCNN(multitask convolution neural network). MTCNN puts face area detection and face key point detection together to realize the calibration of five face feature points. The fatigue state of the driver is judged by opening and closing the eyes and mouth. Finally, the target detection algorithm is used to detect the driver's cell phone calls and smoking. The experiment results show that the system can effectively monitor the driver's status and meet the detection requirements of practical application scenarios.
引用
收藏
页码:823 / 827
页数:5
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