Implementation of Detection Driver Fatigue in Real Time: OpenCV and Keras

Fatigue and drowsiness are major causes of road accidents, particularly during long or late-night drives. Early detection of driver fatigue can significantly reduce such incidents. This project presents a real-time computer vision system that monitors the driver's facial behavior using a webcam. By applying facial landmark detection and calculating the Eye Aspect Ratio (EAR), the system identifies signs of drowsiness such as frequent blinking and prolonged eye closure. These patterns are analyzed frame-by-frame to distinguish between normal blinking and fatigue. When drowsiness is detected, the system triggers an immediate audio alert to regain the driver’s attention. The solution is non-intrusive, requiring no physical contact or sensors, making it practical for regular use. Built using tools like OpenCV, Dlib, and Python, it is cost- effective and scalable for different types of vehicles. With its real-time performance and simple implementation, the system offers a smart approach to improving road safety.