Driver fatigue detection based on improved YOLOv7

Abstract

Fatigue driving is one of the main reasons threatening road traffic safety. Aiming at the problems of complex detection process, low accuracy, and susceptibility to light interference in the current driver fatigue detection algorithm, this paper proposes a driver Eye State detection algorithm based on YOLO, abbreviated as ES-YOLO. The algorithm optimizes the structure of YOLOv7, integrates the multi-scale features using the convolutional block attention mechanism (CBAM), and improves the attention to important spatial locations in the image. Furthermore, using the Focal-EIOU Loss instead of CIOU Loss to increase the attention on difficult samples and reduce the influence of sample class imbalance. Then, based on ES-YOLO, a driver fatigue detection method is proposed, and the driver fatigue judgment logic is designed to monitor the fatigue state in real-time and alarm in time to improve the accuracy of detection. The experiments on the public dataset CEW and the self-made dataset show that the proposed ES-YOLO obtained 99.0% and 98.8% mAP values, respectively, which are better than the compared algorithms. And this method achieves real-time and accurate detection of driver fatigue status. Source code is released in https://www.github/driver-fatigue-detection.git.