Real-Time Drowsiness Detection System

Driver fatigue is a leading cause of road accidents worldwide. Long driving hours, sleep deprivation, night travel, and health conditions significantly reduce the alertness and reaction time of drivers. Traditional safety mechanisms in vehicles focus mainly on collision prevention rather than monitoring the physical condition of the driver. Therefore, this study presents a Real-Time Drowsiness Detection System that continuously monitors the facial features and eye movements of drivers using computer vision and machine learning techniques. The proposed system detects eye closure duration, blinking frequency, and facial fatigue indicators to determine a driver’s alertness level. When drowsiness is detected, the system immediately generates an alert through an alarm or a vibration signal. The system is non-intrusive, cost-effective, and suitable for deployment in real-world settings. The main objective of this study was to develop an accurate, efficient, and real-time monitoring solution that enhances road safety and reduces accident risks.

Drowsiness Detection, Driver Monitoring System, Computer Vision, Eye Blink Detection, Machine Learning, Real-Time Processing, EAR (Eye Aspect Ratio), Road Safety.

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