Driver Drowsiness Detection and Warning System Using Computer Vision and Neural Networks on Embedded Platforms
DOI:
https://doi.org/10.59247/jfsc.v4i2.372Keywords:
Driver Fatigue Detection, Computer Vision, EAR, Deep Learning, Embedded Systems, Raspberry Pi, Neural NetworksAbstract
Driver drowsiness is one of the leading causes of traffic accidents worldwide. Traditional monitoring approaches, such as vehicle-based parameter analysis or physiological signal measurement, often require intrusive sensors or deep access to vehicle systems. To overcome these limitations, this paper proposes a real-time driver drowsiness detection and warning system using computer vision combined with a neural network classifier on an embedded platform. Facial landmarks are extracted using the dlib 68-point model, and the Eye Aspect Ratio (EAR) is computed to evaluate eye-closure behavior. A deep neural classifier is trained on eye-state and temporal EAR sequences collected from 25 subjects to classify normal and drowsy conditions. The system is deployed on a Raspberry Pi 3 B+ embedded platform, integrated with an Arduino-based alarm module to deliver audio–visual alerts when drowsiness is detected. Experimental results demonstrate a training accuracy of 98.4% and a testing accuracy of 92.8% with real-time performance of 15–20 FPS under daylight conditions, stable performance in real time, and feasibility for installation in passenger cars, trucks, and buses. The proposed method contributes a low-cost, efficient, and deployable solution for reducing road accidents with a focus on lightweight embedded implementation.
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Copyright (c) 2026 Chi-Phat Pham, Quang Tran, Binh-Hau Nguyen, Van-Dong-Hai Nguyen, Thi-Hong-Lam Le, Ngoc-Hung Nguyen, Van-Hiep Nguyen, Thanh-Binh Nguyen, Thi-Ngoc-Thao Nguyen, Hoang-Lam Le
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