Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network

• Published in: SN computer science Vol. 1; no. 5; p. 289
• Main Authors: Hashemi, Maryam, Mirrashid, Alireza, Beheshti Shirazi, Aliasghar
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.09.2020; Springer Nature B.V
• Subjects: Accident prevention; Accuracy; Algorithms; Artificial neural networks; Bus drivers; Cameras; Cellular telephones; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Datasets; Deep learning; Driver fatigue; Fatigue; Information Systems and Communication Service; Monitoring systems; Neural networks; Original Research; Pattern Recognition and Graphics; Real time; Sleepiness; Software Engineering/Programming and Operating Systems; Traffic accidents & safety; Vehicle safety; Vision; Drowsiness; Driver; Safety; Convolutional neural networks; Transfer learning
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-020-00306-9

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 classification 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.