@article{article_1705372, title={Real-Time Driver Fatigue Detection and Alert System}, journal={Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji}, volume={13}, year={2025}, DOI={10.29109/gujsc.1705372}, author={Avşar, Murat and Önder, Mithat}, keywords={Gerçek zamanlı sistemler, Sürücü yorgunluk tespiti, Görüntü işleme, Raspberry Pi 4}, abstract={Driver fatigue is one of the critical factors that threaten traffic safety by impairing cognitive performance and prolonging reaction times during driving, thereby posing risks not only to the driver but also to surrounding individuals. Fatigue can nagatively impact driving performance, particularly during long periods of continuous vehicle operation. This study presents the design of a real-time driver fatigue detection system based on Raspberry Pi 4 microcomputer. The system operates by monitoring facial and ocular movements using an in-vehicle camera and analyzing head movements via an MPU6050 accelerometer sensor. Facial detection and eye tracking are performed using the OpenCV library. Through image processing algorithms, the Eye Aspect Ratio(EAR) and the Percentage of Eye Closure(PERCLOS) are calculated to evaluate visual indicators of fatigue. Simultaneously, data obtained from the MPU6050 sensor is used to analyze head tilt, sudden head drops and deviations from the natural head position. These multimodal data streams work in conjunction to support the detection of driver fatigue, forming the basis of an assistive moniroring system.}, number={4}, publisher={Gazi Üniversitesi}