Driver Distraction Behavior Detection Method Based on Deep Learning - Mao, Peng; Zhang, Kunlun; Liang, Da (IOP conference series. Materials Science and Engineering, 2020)
With the rapid development of road traffic in China, driver safety accidents caused by road traffic accidents are increasing year by year. According to statistics of relevant departments, 20%-30% of traffic safety accidents are caused by distracted behaviors of drivers. For this reason, this paper proposes a driver distraction behavior detection method based on deep learning, which uses PCN and DSST algorithms for face detection, location and dynamic face tracking. Finally, YOLOV3 object detection algorithm is used to identify distracting behaviors such as smoking and making phone calls around a person's face. The method can detect distracted behaviors in the driving process in real time and has high detection accuracy.