Deep Learning-based Experimental System Design for Fatigue Driving Detection
Authors: 
Hongmei Jin, Kaihua Cui, Ni Jia, Hao Zhang, Yuying ShiAuthors Info & Claims
Abstract
In order to solve the problem that existing fatigue driving detection methods have high model complexity and are difficult to deploy to embedded devices, this paper designs and implements a deep learning-based fatigue driving detection experimental system. The system integrates YOLOv5 fatigue detection algorithm and Zero-DCE low-light enhancement algorithm, and is deployed on an embedded development board. Firstly, the requirements of the system are analyzed to clarify the functional requirements of the system; secondly, the architecture, functional modules and interfaces of the system are outlined, and the development environment and interface of the fatigue driving detection terminal and the backend management system are designed in detail; finally, the development and implementation of the software system is completed, and the functionality of the system is tested. The system can detect driver fatigue status in real time and, establish visualization of big data Kanban analysis, real-time dynamic analysis of all kinds of fatigue abnormal information to issue timely alarms.
References
[1]
Hui Xu,Zhenyang Zhu,Yingjian Xiao,et al. Fatigue driving detection and application based on face multi-feature points[C]// Proceedings of the Twenty-third Annual Conference on New Network Technologies and Applications, China Computer Users Association Network Application Branch, 2019.2019.
[2]
M. Zhang. Research on fatigue driving detection based on steering wheel grip force [D]. Zhenjiang:Jiangsu University,2016:5.
[3]
YAN Baozhong, WANG Chenzhi, WANG Shuaishuai. Research on fatigue driving detection technology based on human eye characteristics[J]. Applied Science and Technology,2020(1):47-54.
[4]
Olugboja A, Wang Z, Sun Y. Parallel convolutional neural networks for object detection[J]. Journal of Advances in Information Technology Vol, 2021, 12(4).
[5]
Xiao Y, Tian Z, Yu J, A review of object detection based on deep learning[J]. Multimedia Tools and Applications, 2020, 79: 23729-23791.
[6]
Baojun Lai, Gongxing Chen, Shengkai Li, Deep learning based face tracking security monitoring system. Science and Technology Innovation,2020(15):72-74.
[7]
Agrawal A, Jadhav N, Gaur A, Improving the Accuracy of Object Detection in Low Light Conditions using Multiple Retinex Theory-based Image Enhancement Algorithms[C]//2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2022: 1–5.
[8]
Jiang Y, Gong X, Liu D, Enlightengan: Deep light enhancement without paired supervision[J]. IEEE transactions on image processing, 2021, 30: 2340–2349.
[9]
R. Zhao, F. Liu, P. Liu, Helmet detection algorithm based on improved YOLOv5s [J/OL]. Journal of Beijing University of Aeronautics and Astronautics, 2023: 1–16.
[10]
Wang W, Wei C, Yang W, Gladnet: Low-light enhancement network with global awareness[C]//2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018). IEEE, 2018: 751–755.
[11]
Lou P, Yang X, Hu CW, Edge computing based fatigue driving detection method [J]. computer engineering, 2021, 47(07): 13–20.
[12]
Cheng Z, Lin F-S, Jin Chao, Fatigue driving detection based on lightweight convolutional neural network [J]. Journal of Chongqing University of Technology (Natural Science), 2022, 36(02): 142–150.
[13]
Zhaohui Li, Weiliang Zhang. Fatigue Driving Detection Based on Improved YOLOv4 Algorithm [J]. Electronic Measurement Technology, 2021, 44(13): 73–78.
[14]
Yu Shuo, Li Hui, Gui Fangjun, Research on real-time detection algorithm of mask wearing based on YOLOv5 in complex scenes [J]. Computer Measurement & Control, 2021, 29(12): 188–194.
[15]
Zheng Z, Wang P, Liu W, Distance-IoU loss: Faster and better learning for bounding box regression[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(07): 12993–13000.
[16]
Yue X, Li H, Shimizu M, Deep learning-based real-time object detection for empty-dish recycling robot[C]//2022 13th Asian Control Conference (ASCC). IEEE, 2022: 2177–2182.
Index Terms
- Deep Learning-based Experimental System Design for Fatigue Driving Detection
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Published In
November 2023
237 pages
ISBN:9798400709272
DOI:10.1145/3638682
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 May 2024
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VSIP 2023
VSIP 2023: 2023 the 5th International Conference on Video, Signal and Image Processing
November 24 - 26, 2023
Harbin, China
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