Research on Fatigue Driving Detection Method Based on Lightweight Convolutional Neural Network

Abstract

In order to solve the problem of poor real-time performance and low accuracy of a single detection target of common driver fatigue driving detection method based on facial features in practical applications, a fatigue driving detection method based on lightweight convolutional neural network is proposed. First, the driver's facial feature point data set is made through MTCNN (multi task convolutional neural network). Then the data set is used to train a lightweight convolutional neural network to detect the accurate feature point position of the eyes and mouth. Finally, the open and close state of the driver's eyes and mouth is judged based on the feature point coordinates. According to the open and closed state of the eyes and mouth of the continuous multi-frame image, the driver is judged to be in the state of fatigue. The experimental results show that the processing time of the single frame image by the algorithm is 23.3 millisecond; the single detection accuracy is up to 99.4%, and the detection accuracy of fatigue driving can reach 95%. The algorithm is better real-time performance and higher accuracy, so it has certain engineering significance and application prospects.