Modern Drowsiness Detection in Deep Learning: A review

  • Sarah Saadoon Jasim Department of IT, Technical college of Management-Baghdad, Middle Technical University, Baghdad, Iraq
  • Alia Karim Abdul Hassan Department of Computer Science, University of Technology, Baghdad, Iraq
Keywords: driver drowsiness, deep learning, CNN, behavioral techniques, physiological features

Abstract

Recent statistics show that drowsiness is now more of a factor in some car accidents than alcohol. So numerous monitoring systems have been developed to reduce, and accidents eliminate these events. Drowsiness detection systems are plentiful, but it isn't clear which one is the most effective. This review paper will address the following issues: firstly, determining the extracted features, whether behavioral-based, physiological-based, or vehicle-based, which are used to detect driver drowsiness; secondly, focusing on using deep learning to detect driver drowsiness. Finally, concluding from this study that the hybrid-based features should be used Because it gives strength in determining the drowsiness of the driver.

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References

[1] M. F. Shakeel, N. A. Bajwa, A. M. Anwaar, A. Sohail, and A. Khan, "Detecting driver drowsiness in real time through deep learning based object detection," in International Work-Conference on Artificial Neural Networks, 2019: Springer, pp. 283-296.
[2] R. Jabbar, K. Al-Khalifa, M. Kharbeche, W. Alhajyaseen, M. Jafari, and S. Jiang, "Real-time driver drowsiness detection for android application using deep neural networks techniques," Procedia computer science, vol. 130, pp. 400-407, 2018.
[3] P. Thakre, A. Raut, and S. Chavan, "Vision-Based System for Drowsiness Detection," 2021.
[4] J. Cui et al., "A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG," Methods, 2021.
[5] V. Naren Thiruvalar and E. Vimal, "A comparative analysis on driver drowsiness detection using CNN," International Journal of Nonlinear Analysis and Applications, vol. 12, no. Special Issue, pp. 1835-1843, 2021.
[6] M. S. Islalm, M. M. Rahman, M. H. Rahman, M. R. Hoque, A. K. Roonizi, and M. Aktaruzzaman, "A deep learning-based multi-model ensemble method for eye state recognition from EEG," in 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 2021: IEEE, pp. 0819-0824.
[7] A. H. Hasan, A. A. Yasir, and M. J. Hayawi, "Driver Drowsiness Detection Based On the DenseNet 201 Model," Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 13, pp. 3682-3692, 2021.
[8] M. Jonsson and J. Brown, "Deep Learning for Driver Sleepiness Classification using Bioelectrical Signals and Karolinska Sleepiness Scale," ed, 2021.
[9] K. Yasaka and O. Abe, "Deep learning and artificial intelligence in radiology: Current applications and future directions," PLoS Medicine, vol. 15, no. 11, p. e1002707, 2018.
[10] Y. Jiang, M. Yang, S. Wang, X. Li, and Y. Sun, "Emerging role of deep learning‐based artificial intelligence in tumor pathology," Cancer communications, vol. 40, no. 4, pp. 154-166, 2020.
[11] S. S. Jasim and A. K. A. Hassan, "Modern drowsiness detection techniques a review," International Journal of Electrical & Computer Engineering (2088-8708), vol. 12, no. 3, 2022.
[12] Y. Wang, H. Li, C. K. Kirui, and W. Zhang, "Vehicle discrimination using a combined multiple features based on vehicle face," in Proceedings of 2013 Chinese Intelligent Automation Conference, 2013: Springer, pp. 503-511.
[13] A. K. Biswal, D. Singh, B. K. Pattanayak, D. Samanta, and M.-H. Yang, "IoT-based smart alert system for drowsy driver detection," Wireless communications and mobile computing, vol. 2021, 2021.
[14] S. Arefnezhad, S. Samiee, A. Eichberger, M. Frühwirth, C. Kaufmann, and E. Klotz, "Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures," Expert Systems with Applications, vol. 162, p. 113778, 2020.
[15] Y. Jeon, B. Kim, and Y. Baek, "Ensemble CNN to detect drowsy driving with in-vehicle sensor data," Sensors, vol. 21, no. 7, p. 2372, 2021.
[16] N. Peppes, T. Alexakis, E. Adamopoulou, and K. Demestichas, "Driving behaviour analysis using machine and deep learning methods for continuous streams of vehicular data," Sensors, vol. 21, no. 14, p. 4704, 2021.
[17] Y. Moukafih, H. Hafidi, and M. Ghogho, "Aggressive driving detection using deep learning-based time series classification," in 2019 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), 2019: IEEE, pp. 1-5.
[18] J. Cui, Z. Lan, O. Sourina, and W. Müller-Wittig, "EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network," IEEE Transactions on Neural Networks and Learning Systems, 2022.
[19] H. Wendt, G. Geoffroy, L. Chaari, and J.-Y. Tourneret, "Drowsiness Detection Using Joint EEG-ECG Data With Deep Learning," Proc. European Signal Processing Conference (EUSIPCO), 2021.
[20] V. P. Balam, V. U. Sameer, and S. Chinara, "Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram," IET Intelligent Transport Systems, vol. 15, no. 4, pp. 514-524, 2021.
[21] Y.-J. Kang, D.-Y. Kim, and Y.-R. Choi, "AI drowsiness prevention application based on brain waves using deep learning," in Proceedings of the Korea Information Processing Society Conference, 2021: Korea Information Processing Society, pp. 1242-1244.
[22] H. Zeng, C. Yang, G. Dai, F. Qin, J. Zhang, and W. Kong, "EEG classification of driver mental states by deep learning," Cognitive neurodynamics, vol. 12, no. 6, pp. 597-606, 2018.
[23] F. Mohamed, S. F. Ahmed, Z. Ibrahim, and S. Yaacob, "Comparison of features based on spectral estimation for the analysis of EEG signals in driver behavior," in 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), 2018: IEEE, pp. 1-7.
[24] V. R. R. Chirra, S. ReddyUyyala, and V. K. K. Kolli, "Deep CNN: A Machine Learning Approach for Driver Drowsiness Detection Based on Eye State," Rev. d'Intelligence Artif., vol. 33, no. 6, pp. 461-466, 2019.
[25] J. Yu, S. Park, S. Lee, and M. Jeon, "Driver drowsiness detection using condition-adaptive representation learning framework," IEEE transactions on intelligent transportation systems, vol. 20, no. 11, pp. 4206-4218, 2018.
[26] J.-M. Guo and H. Markoni, "Driver drowsiness detection using hybrid convolutional neural network and long short-term memory," Multimedia tools and applications, vol. 78, no. 20, pp. 29059-29087, 2019.
[27] R. Ghoddoosian, M. Galib, and V. Athitsos, "A realistic dataset and baseline temporal model for early drowsiness detection," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019, pp. 0-0.
[28] B. K. Savaş and Y. Becerikli, "Real-time driver fatigue detection system based on multi-task ConNN," Ieee Access, vol. 8, pp. 12491-12498, 2020.
[29] Y. Ed-Doughmi, N. Idrissi, and Y. Hbali, "Real-time system for driver fatigue detection based on a recurrent neuronal network," Journal of imaging, vol. 6, no. 3, p. 8, 2020.
[30] N. Yassine, "Artificial intelligence techniques for driver fatigue detection," Oxford Brookes University, 2020.
[31] I. Nasri, M. Karrouchi, H. Snoussi, K. Kassmi, and A. Messaoudi, "Detection and Prediction of Driver Drowsiness for the Prevention of Road Accidents Using Deep Neural Networks Techniques," in WITS 2020: Springer, 2022, pp. 57-64.
[32] R. Tamanani, R. Muresan, and A. Al-Dweik, "Estimation of driver vigilance status using real-time facial expression and deep learning," IEEE Sensors Letters, vol. 5, no. 5, pp. 1-4, 2021.
[33] P. Liu, H.-L. Chi, X. Li, and J. Guo, "Effects of dataset characteristics on the performance of fatigue detection for crane operators using hybrid deep neural networks," Automation in Construction, vol. 132, p. 103901, 2021.
[34] F. I. Khan, R. "Drowsiness Driver Detection Using Neural Network on UTA-RLDD Dataset." https://github.com/kokfahad/Drowsiness-Driver-Detection---Fahad (accessed 5/17/2022).
[35] H. M. Eraqi, Y. Abouelnaga, M. H. Saad, and M. N. Moustafa, "Driver distraction identification with an ensemble of convolutional neural networks," Journal of Advanced Transportation, vol. 2019, 2019.
[36] L. Barr, S. Popkin, and H. Howarth, "An evaluation of emerging driver fatigue detection measures and technologies," United States. Department of Transportation. Federal Motor Carrier Safety …, 2009.
[37] A. Čolić, O. Marques, and B. Furht, Driver drowsiness detection: Systems and solutions. Springer, 2014.
[38] Q. Abbas, "HybridFatigue: A real-time driver drowsiness detection using hybrid features and transfer learning," International Journal of Advanced Computer Science and Applications, vol. 11, no. 1, p. 9, 2020.
[39] M. Zhu, J. Chen, H. Li, F. Liang, L. Han, and Z. Zhang, "Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network," Neural computing and applications, vol. 33, no. 20, pp. 13965-13980, 2021.
[40] L. Su, "Vision-based Driver State Monitoring Using Deep Learning," University of Waterloo, 2022.
Published
2022-09-23
How to Cite
Jasim, S., & Abdul Hassan, A. (2022). Modern Drowsiness Detection in Deep Learning: A review. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(3), Comp Page 119-129. https://doi.org/10.29304/jqcm.2022.14.3.1023
Section
Computer article