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

被引：0

作者：

Hashemi M. ^{[1
]}

Mirrashid A. ^{[1
]}

Beheshti Shirazi A. ^{[1
]}

机构：

[1] Iran University of Science and Technology, Resalat highway, Tehran

来源：

SN Computer Science | 2020年 / 1卷 / 5期

关键词：

Convolutional neural networks; Driver; Drowsiness; Safety; Transfer learning;

D O I：

10.1007/s42979-020-00306-9

中图分类号：

学科分类号：

摘要：

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. © 2020, Springer Nature Singapore Pte Ltd.

引用

共 35 条

[21]

Sukrit Mehta, Et al., Real-time driver drowsiness detection system using eye aspect ratio and eye closure ratio, Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM)

[22]

Bamidele A., Et al., Non-intrusive driver drowsiness detection based on face and eye tracking, Int J. Adv. Comput. Sci. Appl., 10, (2019)

[23]

Anitha J., Mani G., Venkata R.K., Driver drowsiness detection using Viola Jones algorithm, Smart intelligent computing and applications.ion Smart innovation, systems and technologies, 159, (2020)

[24]

Ji Y., Et al., Fatigue state detection based on multi-index fusion and state recognition network, IEEE Access, 7, (2019)

[25]

Liu W., Et al., Convolutional two-stream network using multifacial feature fusion for driver fatigue detection, Future Internet, 11, 5, (2019)

[26]

Ed-Doughmi Y., Najlae I Driver Fatigue Detection using Recurrent Neural Networks, Proceedings of the 2Nd International Conference on Networking, Information Systems & Security, (2019)

[27]

Wanghua D., Wu R., Real-time driver-drowsiness detection system using facial features, IEEE Access, 7, (2019)

[28]

Jabbar R., Et al., Real-time driver drowsiness detection for android application using deep neural networks techniques, Proc Comput Sci, 130, (2018)

[29]

Viola P., Jones M., Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. I-I, (2001)

[30]

Kazemi V., Sullivan J., One millisecond face alignment with an ensemble of regression trees, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867-1874, (2014)

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