Detection of Driver Drowsiness using Hybrid Learning Model
Keywords:
Eye Aspect Ratio, VGG19, Haar Cascade Algorithm, Siamese NetworkAbstract
In this work, two approaches were explored for detecting driver drowsiness: one utilizing a pre-trained model based on Eye Aspect Ratio combined with the RESNET50 architecture, and the other employing a Hybrid Learning Model integrating Haar Cascade Algorithm with Siamese Networks. First, we implement a traditional approach using a pre-trained RESNET50 model combined with EAR to analyze facial features, mainly focusing on the eye region. This method provides a baseline for comparison with the proposed hybrid model. Next, we proposed a novel technique that combines the robustness of the Haar Cascade (HC) Algorithm for facial feature extraction approach with the effectiveness of Siamese Networks for drowsiness classification. Experimental evaluations are conducted using real-world datasets to compare the performance of both methods in different standard matrices. The results indicate the efficiency of the hybrid model, which outperforms the EAR-based RESNET model in accurately detecting driver drowsiness under various conditions and lighting conditions. Overall, this work contributes to the advancement of driver drowsiness detection systems and develops a novel hybrid model that combines the strengths of traditional computer vision techniques with state-of-the-art methods.
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. M. K. Hussein, T. M. Salman, A. H. Miry and M. A. Subhi, "Driver Drowsiness Detection Techniques: A Survey," 1st Babylon International Conference on Information Technology and Science (BICITS), pp. 45-51, 2021, doi: 10.1109/BICITS51482.2021.9509912.
. H. Laouz, S. Ayad and L. S. Terrissa, "Literature Review on Driver’s Drowsiness and Fatigue Detection," International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, pp. 1-7, 2020, doi: 10.1109/ISCV49265.2020.9204306.
. P. Mate, N. Apte, M. Parate, & S. Sharma, “Detection of driver drowsiness using transfer learning techniques,” Multimedia Tools and Applications, 83(12), pp. 35553-35582, 2024, doi:10.1007/s11042-023-16952-z.
. M. E. Shaik, “A systematic review on detection and prediction of driver drowsiness”, Transportation research interdisciplinary perspectives, 21, 100864, 2023, doi: https://doi.org/10.1016/j.trip.2023.100864.
. R. Hussein, F. S. Miften, L. E. George, “Driver drowsiness detection methods using EEG signals: a systematic review,” Computer methods in biomechanics and biomedical engineering, 26(11), pp: 1237-1249, 2023, doi: 10.1080/10255842.2022.2112574.
. A. Rajkar, N. Kulkarni, & A. Raut, “Driver drowsiness detection using deep learning,” In Applied Information Processing Systems: Proceedings of ICCET 2021 (pp. 73-82), 2022. Springer Singapore.
. E. Magán, M. P. Sesmero, J. M. Alonso-Weber, A. Sanchis, ”Driver drowsiness detection by applying deep learning techniques to sequences of images,” Applied Sciences, 12(3), 1145, 2022, doi:10.3390/app12031145.
. B. Jyothi, K. Seethina, P. Bhavani and C. Jayanth, "Drowsiness Detection of Driver," 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, pp. 1-5, 2022, doi: 10.1109/ICICT55121.2022.10064575.
. A. Altameem, A. Kumar, P. C. Poonia, S. Kumar, & A. K. J. Saudagar, “Early identification and detection of driver drowsiness by hybrid machine learning,” IEEE Access, 9, pp: 162805-162819, 2021.
. A. Wadhwa, & S. S. Roy, “Driver drowsiness detection using heart rate and behavior methods: A study,” Data Analytics in Biomedical Engineering and Healthcare, 163-177, 2021, doi: 10.1016/B978-0-12-819314-3.00011-2.
. A.C. Phan, N. H. Q. Nguyen, T. N. Trieu, & T. C. Phan, “An efficient approach for detecting driver drowsiness based on deep learning,” Applied Sciences, 11(18), 8441, 2021.
. Z. Zhao, N. Zhou, L. Zhang, H. Yan, Y. Xu, Z.; Zhang, “Driver fatigue detection based on convolutional neural networks using EM-CNN. Comput. Intell. Neurosci, pp.72280, 2020, doi: 10.1155/2020/7251280.
. A.C. Phan, N.H.Q. Nguyen, T.N. Trieu, T.C. Phan, “An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning, Appl. Sci, vol. 11, pp-8441, 2021, doi: 10.3390/app11188441.
. A. Rajkar, N. Kulkarni, A. Raut, “Driver drowsiness detection using deep learning. In Applied Information Processing Systems: Proceedings of ICCET 2021; Springer: Berlin/Heidelberg, pp. 73–82, Germany, 2022, doi: 10.1007/978-981-16-2008-9_7.
. M. Hashemi, A. Mirrashid, A. Beheshti Shirazi, “Driver safety development: Real-time driver drowsiness detection system based on convolutional neural network. SN Comput. Sci, vol. 1, pp-289,2020, doi: 10.48550/arXiv.2001.05137.
. S.A. Alameen, A.M. Alhothali, “A Lightweight Driver Drowsiness Detection System Using 3DCNN with LSTM. Comput. Syst. Sci. Eng, vol. 44, pp-895–912, 2023, doi: 10.32604/csse.2023.024643.
. M.W. Gomaa, R.O. Mahmoud, A.M. Sarhan, “A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction. J. Eng. Res, vol. 6, pp-59–70, 2022, doi: 10.21608/erjeng.2022.141514.1067.
. J. Singh, R. Kanojia, R. Singh, R. Bansal, S. Bansal, “Driver Drowsiness Detection System: An Approach by Machine Learning Application. arXiv 2023, arXiv:2303, doi: 10.48550/arXiv.2303.06310.
. M. Tibrewal, A. Srivastava, R. Kayalvizhi, “A deep learning approach to detect driver drowsiness. Int. J. Eng. Res. Technol, vol. 10, pp-183–189, 2021, doi: 10.3390/safety9030065.
. V. Jothsna, I. Patel, K. Raghu, P. Jahnavi, K. N.. Reddy, & K. Saikumar, “A fuzzy expert system for the drowsiness detection from blink characteristics, “In 2021 7th international conference on advanced computing and communication systems (ICACCS) (Vol. 1, pp. 1976-1981), 2021, IEEE.
. E. Magán, A. Ledezma, P. Sesmero, A. Sanchis, “Fuzzy Alarm System based on Human-centered Approach. In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2020), Prague, Czech Republic, pp. 448–455, 2-4 May 2020, doi: 10.5220/0009348704480455.
. I. Jahan, K. A. Uddin, S. A. Murad, M. S. A. Miah, T. Z. Khan, M. Masud, & A. K. Bairagi, “4D: a real-time driver drowsiness detector using deep learning,” Electronics, 12(1), 235, 2023.
. G. Yogarajan, R. N. Singh, S. A. Nandhu, & R. M. Rudhran, “Drowsiness detection system using deep learning based data fusion approach,” Multimedia Tools and Applications, 1-15, 2023.
. S.A., El-Nabi, W. El-Shafai, E. S. M., El-Rabaie, K. F., Ramadan, F. E., Abd El-Samie, S., & Mohsen, S. “Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review”. Multimedia Tools and Applications, 83(3), 9441-9477, 2024,doi: 10.1007/s11042-023-15054-0.
. M., Faisal, & S., Rizvi. “Identification and Detection of Driver Drowsiness using Machine Learning Techniques”, Journal of Management and Service Science (JMSS), 4(1), 1-10, 2024, doi: 10.54060/a2zjournals.jmss.58.
. U. P. Lakshmi, P. V. S. Srinivas, S. Shyam, M. R. Muchintala, V. R. Palugulla, &H. Y. Mandra, “Driver Drowsiness Detection using Hybrid Algorithm,” Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 12(1), 171-178, 2024, doi: 10.52549/ijeei.v12i1.4738.
. Q. Abbas, “HybridFatigue: A real-time driver drowsiness detection using hybrid features and transfer learning,” International Journal of Advanced Computer Science and Applications, 11(1), 2020.
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