Deep learning based Driver Distraction and Drowsiness Detection

This paper presents a novel approach and a new dataset for the problem of driver drowsiness and distraction detection. Lack of an available and accurate eye dataset strongly feels in the area of eye closure detection. Therefore, a new comprehensive dataset is proposed, and a study on driver distraction of the road is provided to supply safety for the drivers. A deep network is also designed in such a way that two goals of real-time application, including high accuracy and fastness, are considered simultaneously. The main purposes of this article are as follows: Estimation of driver head direction for distraction detection, introduce a new comprehensive dataset to detect eye closure, and also, presentation of three networks in which one of them is a fully designed deep neural network (FD-DNN) and others use transfer learning with VGG16 and VGG19 with extra designed layers (TL-VGG). The experimental results show the high accuracy and low computational complexity of the estimations and the ability of the proposed networks on drowsiness detection.