A review of techniques and methods for deep learning techniques in driver fatigue detection

Research Article
Open access

A review of techniques and methods for deep learning techniques in driver fatigue detection

Yawen Luo 1*
  • 1 School of Computing and software system, University of Melbourne, Parkville, Victoria, 3010, Australia    
  • *corresponding author 1548183470@qq.com
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/31/20230119
ACE Vol.31
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-287-9
ISBN (Online): 978-1-83558-288-6

Abstract

Road accidents in which fatigue driving is a significant cause of death are responsible for many deaths worldwide. Approximately 100,000 crashes are caused by driver fatigue each year. Also, fatigue driving is responsible for about 16% of road accidents in general and more than 20% of highway accidents, so fatigue driving accounts for a large percentage of vehicle accidents. Fatigue driving detection usually uses subjective and objective methods. Subjective methods rely on analysing the driver's psychological and facial expression information, while objective methods use external devices to extract feature parameters and apply artificial intelligence algorithms. However, these methods have limitations, such as subjectivity and individual differences. Deep learning, a promising tool inspired by neural networks, offers automatic feature learning, robust pattern recognition, and high adaptability. This review explores the application of deep learning in fatigue driving detection. It examines various deep learning feature extraction methods, classification models, prediction models, and related datasets. By leveraging deep learning techniques, fatigue driving detection can achieve higher accuracy and effectiveness, providing a reliable solution to this critical road safety problem. The review concludes with recommendations and future perspectives in this area.

Keywords:

Fatigue Driving, Deep Learning, Driver Monitoring, Feature Extraction, Classification, And Recognition Methods

Luo,Y. (2024). A review of techniques and methods for deep learning techniques in driver fatigue detection. Applied and Computational Engineering,31,36-42.
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References

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[9]. S. Anber, W. Alsaggaf, and W. Shalash, “A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features,” Electronics, vol. 11, no. 2, p. 285, Jan. 2022, doi: https://doi.org/10.3390/electronics11020285.

[10]. G. Zhao, Y. He, H. Yang, and Y. Tao, “Research on fatigue detection based on visual features,” IET Image Processing, vol. 16, no. 4, pp. 1044–1053, Apr. 2021, doi: https://doi.org/10.1049/ipr2.12207.

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[12]. M. Liu, X. Xu, J. Hu, and Q. Jiang, “Real time detection of driver fatigue based on CNN‐LSTM,” IET Image Processing, Nov. 2021, doi: https://doi.org/10.1049/ipr2.12373.

[13]. X. Li, “Fatigue Detection Based on Multimodal Fusion Neural Network,” IEEE Xplore, Feb. 01, 2023. https://ieeexplore.ieee.org/abstract/document/10145407/authors#authors (accessed Jun. 26, 2023).

[14]. J. Shotton et al., “Real-time human pose recognition in parts from single depth images,” IEEE Xplore, Jun. 01, 2011. https://ieeexplore.ieee.org/abstract/document/5995316 (accessed May 20, 2021).

[15]. V. Jain and E. Learned-Miller, “FDDB: A Benchmark for Face Detection in Unconstrained Settings.” Accessed: Oct. 18, 2019. [Online]. Available: http://vis-www.cs.umass.edu/fddb/fddb.pdf

[16]. J. Yan, X. Zhang, Z. Lei, and S. Z. Li, “Face detection by structural models,” Image and Vision Computing, vol. 32, no. 10, pp. 790–799, Oct. 2014, doi: https://doi.org/10.1016/j.imavis.2013.12.004.

[17]. Q. Massoz, T. Langohr, Clémentine François, and J. Verly, “The ULg multimodality drowsiness database (called DROZY) and examples of use,” ORBi (University of Liège), Mar. 2016, doi: https://doi.org/10.1109/wacv.2016.7477715.

[18]. C. B. S. Maior, M. J. das C. Moura, J. M. M. Santana, and I. D. Lins, “Real-time classification for autonomous drowsiness detection using eye aspect ratio,” Expert Systems with Applications, vol. 158, p. 113505, Nov. 2020, doi: https://doi.org/10.1016/j.eswa.2020.113505.

[19]. S. Abtahi, M. Omidyeganeh, S. Shirmohammadi, and B. Hariri, “YawDD,” Proceedings of the 5th ACM Multimedia Systems Conference on - MMSys ’14, 2014, doi: https://doi.org/10.1145/2557642.2563678.

Cite this article

Luo,Y. (2024). A review of techniques and methods for deep learning techniques in driver fatigue detection. Applied and Computational Engineering,31,36-42.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-287-9(Print) / 978-1-83558-288-6(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.31
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. S. M. Marcora, W. Staiano, and V. Manning, “Mental fatigue impairs physical performance in humans,” Journal of Applied Physiology, vol. 106, no. 3, pp. 857–864, Mar. 2009, doi: https://doi.org/10.1152/japplphysiol.91324.2008.

[2]. J. A. Horne and L. A. Reyner, “Sleep related vehicle accidents,” BMJ, vol. 310, no. 6979, pp. 565–567, Mar. 1995, doi: https://doi.org/10.1136/bmj.310.6979.565.

[3]. H. Abdul Rahim, A. Dalimi, and H. Jaafar, “Detecting Drowsy Driver Using Pulse Sensor,” Jurnal Teknologi, vol. 73, no. 3, Mar. 2015, doi: https://doi.org/10.11113/jt.v73.4238.

[4]. R. Hooda, V. Joshi, and M. Shah, “A comprehensive review of approaches to detect fatigue using machine learning techniques,” Chronic Diseases and Translational Medicine, Aug. 2021, doi: https://doi.org/10.1016/j.cdtm.2021.07.002.

[5]. J. Koushik, “Understanding Convolutional Neural Networks,” arXiv:1605.09081 [stat], May 2016, Available: https://arxiv.org/abs/1605.09081

[6]. X.-M. Zhu, W.-L. Zheng, B.-L. Lu, X. Chen, S. Chen, and C. Wang, “EOG-based drowsiness detection using convolutional neural networks,” International Joint Conference on Neural Network, Jul. 2014, doi: https://doi.org/10.1109/ijcnn.2014.6889642.

[7]. Z. Zhao, N. Zhou, L. Zhang, H. Yan, Y. Xu, and Z. Zhang, “Driver Fatigue Detection Based on Convolutional Neural Networks Using EM-CNN,” Computational Intelligence and Neuroscience, vol. 2020, p. e7251280, Nov. 2020, doi: https://doi.org/10.1155/2020/7251280.

[8]. S. Masood, A. Rai, A. Aggarwal, M. N. Doja, and M. Ahmad, “Detecting distraction of drivers using Convolutional Neural Network,” Pattern Recognition Letters, Jan. 2018, doi: https://doi.org/10.1016/j.patrec.2017.12.023.

[9]. S. Anber, W. Alsaggaf, and W. Shalash, “A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features,” Electronics, vol. 11, no. 2, p. 285, Jan. 2022, doi: https://doi.org/10.3390/electronics11020285.

[10]. G. Zhao, Y. He, H. Yang, and Y. Tao, “Research on fatigue detection based on visual features,” IET Image Processing, vol. 16, no. 4, pp. 1044–1053, Apr. 2021, doi: https://doi.org/10.1049/ipr2.12207.

[11]. O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, Apr. 2015, doi: https://doi.org/10.1007/s11263-015-0816-y.

[12]. M. Liu, X. Xu, J. Hu, and Q. Jiang, “Real time detection of driver fatigue based on CNN‐LSTM,” IET Image Processing, Nov. 2021, doi: https://doi.org/10.1049/ipr2.12373.

[13]. X. Li, “Fatigue Detection Based on Multimodal Fusion Neural Network,” IEEE Xplore, Feb. 01, 2023. https://ieeexplore.ieee.org/abstract/document/10145407/authors#authors (accessed Jun. 26, 2023).

[14]. J. Shotton et al., “Real-time human pose recognition in parts from single depth images,” IEEE Xplore, Jun. 01, 2011. https://ieeexplore.ieee.org/abstract/document/5995316 (accessed May 20, 2021).

[15]. V. Jain and E. Learned-Miller, “FDDB: A Benchmark for Face Detection in Unconstrained Settings.” Accessed: Oct. 18, 2019. [Online]. Available: http://vis-www.cs.umass.edu/fddb/fddb.pdf

[16]. J. Yan, X. Zhang, Z. Lei, and S. Z. Li, “Face detection by structural models,” Image and Vision Computing, vol. 32, no. 10, pp. 790–799, Oct. 2014, doi: https://doi.org/10.1016/j.imavis.2013.12.004.

[17]. Q. Massoz, T. Langohr, Clémentine François, and J. Verly, “The ULg multimodality drowsiness database (called DROZY) and examples of use,” ORBi (University of Liège), Mar. 2016, doi: https://doi.org/10.1109/wacv.2016.7477715.

[18]. C. B. S. Maior, M. J. das C. Moura, J. M. M. Santana, and I. D. Lins, “Real-time classification for autonomous drowsiness detection using eye aspect ratio,” Expert Systems with Applications, vol. 158, p. 113505, Nov. 2020, doi: https://doi.org/10.1016/j.eswa.2020.113505.

[19]. S. Abtahi, M. Omidyeganeh, S. Shirmohammadi, and B. Hariri, “YawDD,” Proceedings of the 5th ACM Multimedia Systems Conference on - MMSys ’14, 2014, doi: https://doi.org/10.1145/2557642.2563678.

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