Driver Safety Development Real Time Driver Drowsiness Detection System Based on Convolutional Neural Network
This addresses driver safety by providing a drowsiness detection system, but it is incremental as it builds on existing CNN methods with a new dataset.
The paper tackles driver drowsiness detection by developing a system using Convolutional Neural Networks (CNN) to classify eye status, achieving high accuracy and low computational complexity for real-time application.
This paper focuses on the challenge of driver safety on the road and presents a novel system for driver drowsiness detection. In this system, to detect the falling sleep state of the driver as the sign of drowsiness, Convolutional Neural Networks (CNN) are used with regarding the two goals of real-time application, including high accuracy and fastness. Three networks introduced as a potential network for eye status classifcation in which one of them is a Fully Designed Neural Network (FD-NN) and others use Transfer Learning in VGG16 and VGG19 with extra designed layers (TL-VGG). Lack of an available and accurate eye dataset strongly feels in the area of eye closure detection. Therefore, a new comprehensive dataset proposed. The experimental results show the high accuracy and low computational complexity of the eye closure estimation and the ability of the proposed framework on drowsiness detection.