Identification methods for driver’s dangerous driving behavior in low visibility environments

Identifying driving behaviors in low visibility is crucial for enhancing road safety, reducing accidents, and improving emergency response, ensuring driver's adaptability and safety on roads. In view of the problems of low identification accuracy and long identification time in low visibility environment, a dangerous driving behavior identification system based on machine vision is constructed in this paper. First, the driver image is collected and the low-light image enhancement processing is realized using KinD network. Then, the driver's facial feature points are captured to realize the extraction of dangerous driving behavior features. Finally, the YOLOv5s model is improved to improve the loss function, introduce Focal loss and attention mechanism modules to improve the recognition accuracy and feature extraction ability, and realize the effective identification of dangerous driving behavior. The experimental results show that the recognition accuracy of this method is more than 96%, the recognition time is short, and always keep within 80ms, with high identification efficiency and good application effect.