Fatigue Driving Detection Based on Deep Learning and Multi-Index Fusion

Abstract

In order to reduce traffic accidents caused by fatigue driving, a fatigue driving detection algorithm is proposed based on deep learning and facial multi-index fusion from the driver $'\text{s}$ facial features. Because the scene in the actual driving process is very complex and changeable, this algorithm first improves the multi-task cascaded convolutional neural network (MTCNN) so that it can quickly and accurately locate the face and detect the facial key points. According to the facial key points, the driver’s eyes and mouth regions are determined. Second, these regions are input into the eyes and mouth state recognition network (E-MSR Net) for state recognition. The E-MSR Net is a depth separable convolution neural network that is improved and optimized based on MobilenetV2. Finally, the three facial features of eye closure rate (ECR), mouth opening rate (MOR), and head non-positive face rate (HNFR) are fused to judge the driver’s fatigue state. This algorithm can quickly and accurately make judgments in the face of complex and changeable scenes. At the same time, it can avoid the failure of the algorithm caused by the occlusion of the eyes or mouth due to wearing sunglasses or masks during driving. The accuracy of the proposed algorithm on the self-made data set achieved 97.5%, which proved the feasibility of the algorithm.

Keywords

• Fatigue driving detection
• improved MTCNN
• E-MSR Net
• facial multi-index fusion