A Real-Time Driver Drowsiness Detection Using Computer Vision and Machine Learning
DOI:
https://doi.org/10.69996/fmep.2026011Keywords:
Driver Drowsiness Detection, Augmentation, YOLOv-8, Haar Cascade, Real-Time MonitoringAbstract
Driver drowsiness is one of the main factors that lead to road accidents, thereby causing serious injuries and deaths. Driver monitoring systems that are traditionally adopted include physical or physiological measures like EEG, ECG, or wearables, which are cumbersome, expensive, and not suitable for practical use. In order to overcome such disadvantages, a new non-intrusive approach for driver drowsiness detection based on their vision is presented. In the proposed approach, Haar cascade classifiers are utilized, resulting in effective facial feature detection like face, eye, and mouth, based on live webcam feed. All detected regions are further processed using a YOLO-based classification model for classifying and deciding the driver’s status like eye-open, eye-closed, yawn, and combined conditions of eye closed with a yawn. On the basis of these classifications, the level of alertness of the driver is effectively determined. In case of drowsiness, a three-level alert system is implemented in the system, viz. buzzer sound, voice, and video recording. If drowsiness persists, the recorded video is saved and sent via email.
References
[1] S. Essahraui, I. Lamaakal, I. El Hamly, Y. Maleh, I. Ouahbi et al., “Real-time driver drowsiness detection using facial analysis and machine learning techniques,” Sensors, vol. 25, no. 3, pp. 812, 2025.
[2] O.F. Hassan, A. F. Ibrahim, A. Gomaa, M. A. Makhlouf and B. Hafiz, “Real-time driver drowsiness detection using transformer architectures,” Scientific Reports, vol. 15, pp. 17493, 2025.
[3] A. Jarndal, H. Tawfik, A.I. Siam, I. Alsyouf and A. Cheaitou, “A vision transformers-based system for enhanced driver drowsiness detection,” IEEE Access, 2024.
[4] C. Srinithi, G. Lathaselvi and A. Swetha, “Predicting driver drowsiness using CNN and real-time facial detection,” Proc. ICCRTEE, pp. 1–6, 2025.
[5] Y. Jiao, “Driver fatigue detection using heart rate variability and electrodermal activity,” Sensors, vol. 23, 2023.
[6] H.V. Chand, “Driver drowsiness detection system using emotion analysis,” International Journal of Intelligent Systems, 2022.
[7] X. Zhang, “PERCLOS-based driver fatigue monitoring system,” IEEE Trans. Intelligent Vehicles, 2021.
[8] G. Kaur, “Deep learning-based driver yawning detection,” Pattern Recognition Letters, 2021.
[9] Y. Pan, “YOLO-based driver monitoring system,” Journal of Real-Time Image Processing, 2020.
[10] M. Abtahi, S. Hariri and S. Shirmohammadi, “Driver drowsiness monitoring based on yawning detection,” IEEE Instrumentation & Measurement Magazine, 2014.
[11] A. V. Orosco, J. A. Gonzalez, and E. Laciar, “Blink rate analysis for driver drowsiness detection,” International Journal of Pattern Recognition and Artificial Intelligence, 2014.
[12] S. Park, F. Pan, S. Kang and C. D. Yoo, “Driver drowsiness detection system based on feature representation learning using various deep networks,” Asian Conference on Computer Vision, 2016.
[13] Swara Snehit Patil, “Artificial Intelligence: A Way to Promote Innovation”, JSIHS, vol. 2, no. 1, pp. 1–5, Mar. 2024.
[14] Kavin Ramakrishnan and Velmurugan S, “Machine Learning and Sensor-Based Approaches for Driver Fatigue and Drowsiness Detection: A Mini Review”, JCAI, vol. 4, no. 1, pp. 1–10, Feb. 2026.
[15] Godfrey Wandwi and Gwenda Wandwi, “Deep Learning Architectures for Early Detection of Diabetic Retinopathy in Retinal Image Analysis”, JCAI, vol. 3, no. 5, pp. 68–75, Oct. 2025.
