Research on fatigue driving detection based on deep learning face key point algorithm

Driver fatigue is one of the leading causes of traffic accidents and poses a serious threat to traffic safety. Therefore, realizing real-time monitoring and early warning of driver fatigue status is especially important. Currently, most fatigue driving detection technologies are based on physiological signal detection, but this method cannot accurately determine fatigue behavior, and there are limitations to the use of high sensor costs. To this end, this paper proposes a fatigue determination algorithm based on deep learning face key point detection. The algorithm uses the RetinaFace network for face detection and intercept, the PFLD face key point detection network to extract facial key points and calculates fatigue indicators based on these key points to achieve the detection of driver fatigue state by integrating multiple evaluation criteria. The experimental results show that the algorithm has significant accuracy and detection speed advantages, providing a new technical solution for realizing real-time monitoring and early warning of fatigue driving.