R2DS: A novel hierarchical framework for driver fatigue detection in mountain freeway.

Abstract

Fatigue driving is one of the main factors which affect the safety of drivers and passengers in mountain freeway. To improve the driving safety, the application of fatigue driving detection system is a crucial measure. Accuracy, speed and robustness are key performances of fatigue detection system. However, most researches pay attention to one of them, instead of taking care of them all. It has limitation in practical application. This paper proposes a novel three-layered framework, named Real-time and Robust Detection System. Specifically, the framework includes three modules, called facial feature extraction, eyes regions extraction and fatigue detection. In the facial feature extraction module, the paper designs a deep cascaded convolutional neural network to detect the face and locate eye key points. Then, a face tracking sub-module is constructed to increase the speed of the algorithm, and a face validation submodule is applied to improve the stability of detection. Furthermore, to ensure the orderly operation of each sub-module, we designed a recognition loop based on the finite state machine. It can extract facial feature of the driver. In the second module, eyes regions of the driver were captured according to the geometric feature of face and eyes. In the fatigue detection module, the ellipse fitting method is applied to obtain the shape of driver's pupils. According to the relationship between the long and short axes of the ellipse, eyes state (opening or closed) can be decided. Lastly, the PERCLOS, which is defined by calculating the number of closed eyes in a period, is used to determine whether fatigue driving or not. The experimental results show that the comprehensive accuracy of fatigue detection is 95.87%. The average algorithm rate is 32.29 ms/f in an image of 640×480 pixels. The research results can serve the design of a new generation of driver fatigue detection system to mountain freeway.