Driver Fatigue Detection Using Improved Deep Learning and Personalized Framework

Abstract

In transportation, drivers’ state directly affects traffic safety. Therefore, an accurate driver’s fatigue detection is crucial for ensuring driving safety. Real-time and accurate technology is needed for driver fatigue detection. To address this problem, this article proposes a fatigue detection method based on an improved deep learning and personalized framework. First, clustering is applied to face size, and cluster numbers are used to determine the number of detection layers. Then, the size of anchor boxes is set according to the face size. In the proposed framework, the number of convolutional networks is set according to the principle that the receptive field should match the face size in the predicted feature map. Finally, a variety of fatigue features are learned by minimizing the loss function. In addition, a personalized face fatigue detection method is put forward for building a fatigue detection framework to judge the driver’s fatigue status more reasonably. The experimental results show that the proposed method based on an improved clustering method and local receptive field can improve the detection speed of driver’s fatigue while maintaining high detection accuracy. The proposed method can reach 125 fps by using GPU GeForce GTX TITAN, which satisfies the real-time requirement. In addition, the personalized framework can achieve high detection accuracy while keeping acceptable speed. The proposed model can accurately and timely detect driver fatigue, which can help to avoid accidents.