Abstract
Driver drowsiness is a major cause of road acci- dents, especially during long-distance and night-time driving. Fatigue severely degrades reaction time, decision-making ability, and situational awareness, leading to life-threatening incidents. Existing detection techniques such as physiological sensing and vehicle behavior monitoring often suffer from intrusiveness or environmental sensitivity. This paper presents a real-time hybrid drowsiness detection system combining the speed of Haar Cascade classifiers with the accuracy of a Convolutional Neural Network (CNN) for open/closed eye-state classification. The system continuously monitors eye regions and evaluates prolonged eye closure using temporal thresholding to determine the onset of drowsiness. The lightweight design ensures real-time performance at 24–28 FPS on standard CPU hardware, achieving an accuracy of 96%. The proposed system effectively addresses limitations in classical EAR-based systems identified in previous research, including issues with illumination, spectacles reflection, and head movement. Experimental results demonstrate strong robustness and make the approach suitable for integration into low-cost ADAS systems.
Keywords
Drowsiness Detection, CNN, Haar Cascade, Eye- State Classification, Computer Vision, Deep Learning.
Citations
IRE Journals:
Sufiya Begum, Safdar Iqbal, Mohammad Foorqan, Md Zahoor Rahi, Md Asif Alam "Real-Time Drowsiness Detection" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 140-145 https://doi.org/10.64388/IREV9I6-1712571
IEEE:
Sufiya Begum, Safdar Iqbal, Mohammad Foorqan, Md Zahoor Rahi, Md Asif Alam
"Real-Time Drowsiness Detection" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712571
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