i-manager Publications

A Review on the Effectiveness of Driver Drowsiness Detection System using Computerised Devices

Louis Twali Mugisha*, G. Glorindal**

*-** DMI - St. John the Baptist University, Mangochi, Malawi.

Periodicity:July - September'2023
DOI : https://doi.org/10.26634/jip.10.3.20055

Abstract

Driver drowsiness is a critical factor contributing to road accidents worldwide, with potentially devastating consequences. To mitigate this problem, numerous driver drowsiness detection systems have been developed, employing various computerized devices and technologies. The development of technologies for detecting drowsiness is a major challenge in the field of accident-avoidance systems. Since the dangers of drowsiness exist on the road, techniques need to be advanced to prevent its consequences. The most crucial aspect of this work is to develop a drowsiness detection system by tracking the driver's eyes. It is believed that signs of driver exhaustion can be detected early enough to prevent a car accident. This technology aims to enhance road safety by alerting drivers or triggering safety measures when drowsiness is detected, ultimately saving lives and preventing accidents. Detecting fatigue involves observing eye movements and blink patterns. This review aims to provide an in-depth analysis of the effectiveness of these systems in identifying and alerting drowsy drivers, ultimately enhancing road safety. The analysis of facial images is a popular research area with applications such as face recognition and human identification security systems. Furthermore, this review delves into the various methodologies and algorithms used in driver drowsiness detection, including machine learning, image analysis, and signal processing techniques. It also highlights the need for further research to address the challenges and limitations faced by these systems and suggests potential avenues for improvement.

Keywords

Deep Learning, Open Cv, Tensor Flow, Machine Language Libraries, Pygame, Driver Drowsiness Detection System, Computerized Devices, Vehicle Safety.

How to Cite this Article?

Mugisha, L. T., and Glorindal, G. (2023). A Review on the Effectiveness of Driver Drowsiness Detection System using Computerised Devices. i-manager’s Journal on Image Processing, 10(3), 17-26. https://doi.org/10.26634/jip.10.3.20055

References

[1]. Albadawi, Y., Takruri, M., & Awad, M. (2022). A review of recent developments in driver drowsiness detection systems. Sensors, 22(5), 2069.

[2]. Arceda, V. M., Nina, J. C., & Fabian, K. F. (2020, November). A sur vey on drowsiness detection techniques. In Iberoamerican Conference of Computer Human Interaction, Arequipa, Perú, 15, 1-10.

[3]. Austin, R., Lobo, F., & Rajaguru, S. (2021). GSM and arduino based vital sign monitoring system. The Open Biomedical Engineering Journal, 15(1), 78-89.

[4]. Bajaj, J. S., Kumar, N., & Kaushal, R. K. (2021, March). Comparative study to detect driver drowsiness. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 678-683). IEEE.

[5]. Bajaj, J. S., Kumar, N., & Kaushal, R. K. (2022). AI based novel approach to detect driver drowsiness. ECS Transactions, 107(1), 4651.

[6]. Bajaj, J. S., Kumar, N., & Kaushal, R. K. (2022). Feasibility study on amalgamation of multiple measures to detect driver drowsiness. ECS Transactions, 107(1), 1951.

[7]. Cheng, B., Zhang, W., Lin, Y., Feng, R., & Zhang, X. (2012). Driver drowsiness detection based on multisource information. Human Factors and Ergonomics in Manufacturing & Service Industries, 22(5), 450-467.

[8]. Čolić, A., Marques, O., Furht, B., Čolić, A., Marques, O., & Furht, B. (2014). Driver drowsiness detection and measurement methods. In Driver drowsiness detection: Systems and solutions. Springer(pp.7-18).

[9]. Deshmukh, A.D., G Nakrani, M., L Bhuyar, D., & B Shinde, U. (2019, February). Face recognition using OpenCv based on IoT for smart door. In Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India (pp. 1066-1073).

[10]. Dhruba, A. R., Alam, K. N., Khan, M. S., Bourouis, S., & Khan, M. M. (2021). Development of an IoT-based sleep apnea monitoring system for healthcare applications. Computational and Mathematical Methods in Medicine.

[11]. Doudou, M. S., Bouabdallah, A., & Cherfaoui, V. (2018). A light on physiological sensors for efficient driver drowsiness detection system. Sensors & Transducers Journal, 224(8), 39-50.

[12]. Doudou, M., Bouabdallah, A., & Berge-Cherfaoui, V. (2020). Driver drowsiness measurement technologies: Current research, market solutions, and challenges. International Journal of Intelligent Transportation Systems Research, 18, 297-319.

[13]. Gwak, J., Shino, M., & Hirao, A. (2018, November). Early detection of driver drowsiness utilizing machine learning based on physiological signals, behavioral measures, and driving performance. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 1794-1800). IEEE.

[14]. Horne, J. A., & Baulk, S. D. (2004). Awareness of sleepiness when driving. Psychophysiology, 41(1), 161-165.

[15]. Jabbar, R., Al-Khalifa, K., Kharbeche, M., Alhajyaseen, W., Jafari, M., & Jiang, S. (2018). Real-time driver drowsiness detection for android application using deep neural networks techniques. Procedia Computer Science, 130, 400-407.

[16]. Liu, W., Qian, J., Yao, Z., Jiao, X., & Pan, J. (2019). Convolutional two-stream network using multi-facial feature fusion for driver fatigue detection. Future Internet, 11(5), 115.

[17]. Murugan, S., Selvaraj, J., & Sahayadhas, A. (2019, March). Analysis of different measures to detect driver states: A review. In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-6). IEEE.

[18]. Ngxande, M., Tapamo, J. R., & Burke, M. (2017). Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques. In Proceedings of the 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA- Robmech) (pp. 156-161).

[19]. Perkins, E., Sitaula, C., Burke, M., & Marzbanrad, F. (2022). Challenges of driver drowsiness prediction: The remaining steps to implementation. IEEE Transactions on Intelligent Vehicles, 8(2), 1319-1338.

[20]. Raspberry Pi. (2022). Raspberry Pi 3 Model B+.

[21]. Rosebrock, A. (2017). Eye Blink Detection with Opencv, Python, and Dlib.

[22]. Sahayadhas, A., Sundaraj, K., & Murugappan, M. (2012). Detecting driver drowsiness based on sensors: a review. Sensors, 12(12), 16937-16953.

[23]. Sakshi, & Kukreja, V. (2023). A dive in white and grey shades of ML and non-ML literature: A multivocal analysis of mathematical expressions. Artificial Intelligence Review, 56(7), 7047-7135.

[24]. Savaş, B. K., & Becerikli, Y. (2020). Real time driver fatigue detection system based on multi-task ConNN. IEEE Access, 8, 12491-12498.

[25]. Seed Studio. (2022). Grove - GSR Sensor.

[26]. Shahrudin, N. N., & Sidek, K. A. (2020, March). Driver drowsiness detection using different classification algorithms. In Journal of Physics: Conference Series, 1502(1), 012037. IOP Publishing.

[27]. Sharma, M., Kacker, S., & Sharma, M. (2016). A brief introduction and review on galvanic skin response. International Journal of Medical Research Profesionals, 2(6), 13-17.

[28]. Shi, W., Li, J., & Yang, Y. (2020). Face fatigue detection method based on MTCNN and machine vision. In International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019: Applications and Techniques in Cyber Intelligence, 7, 233-240. Springer International Publishing.

[29]. Singh, N., & Brisilla, R. M. (2021, November). Comparison analysis of different face detecting techniques. In 2021 Innovations in Power and Advanced Computing Technologies (i-PACT) (pp. 1-6). IEEE.

[30]. Soares, S., Monteiro, T., Lobo, A., Couto, A., Cunha, L., & Ferreira, S. (2020). Analyzing driver drowsiness: From causes to effects. Sustainability, 12(5), 1971.

[31]. Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE.

[32]. Weng, C. H., Lai, Y. H., & Lai, S. H. (2017). Driver drowsiness detection via a hierarchical temporal deep belief network. In Computer Vision–ACCV 2016 Workshops: ACCV 2016 International Workshops, Taipei, Taiwan (pp. 117-133). Springer International Publishing.

[33]. World Health Organisation. (2002). Road Traffic Injuries.

[34]. Yang, G., Lin, Y., & Bhattacharya, P. (2010). A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Information Sciences, 180(10), 1942-1954.

[35]. Yongcun, W., & Jianqiu, D. (2021, March). Online examination behavior detection system for preschool education professional skills competition based on MTCNN. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering(ICBAIE)(pp.999-1005). IEEE.

[36]. Siddiqui, H. U. R., Saleem, A. A., Brown, R., Bademci, B., Lee, E., Rustam, F., & Dudley, S. (2021). Non-invasive driver drowsiness detection system. Sensors, 21(14), 4833.

[37]. Phan, A. C., Nguyen, N. H. Q., Trieu, T. N., & Phan, T. C. (2021). An efficient approach for detecting driver drowsiness based on deep learning. Applied Sciences, 11(18), 8441.