A fatigue driving detection algorithm based YOLOv5

Abstract

Aiming at the problems of slow detection speed and low detection accuracy in existing fatigue driving detection algorithms, a fatigue driving detection algorithm based on YOLOv5 is proposed. In order to improve the feature extraction ability of the network, the convolution module is used to replace the slice structure in Backbone; the algorithm combines the data features of fatigue driving images to simplify and optimize the Neck structure of the YOLOv5 model, which will be suitable for detecting 19×19 features of larger size objects. Graph branch pruning to reduce model complexity and improve real-time detection. Finally, on the basis of facial feature extraction, the algorithm determines the state of fatigue features according to PERCLOS and POM parameters combined with thresholds and outputs the results. The experimental results on the NHTU-DDD data set show that the accuracy of the detection model reaches 95.25%, the model size is only 10MB, and the single-frame detection speed is 9ms, which is 21.5% higher than the original YOLOv5 algorithm. At the same time, the model parameters are greatly reduced, which can better meet the real-time requirements of fatigue detection application scenarios.