DRIVER DROWSINESS DETECTION SYSTEM

Driver drowsiness is a major cause of road accidents worldwide, often resulting in serious injuries or fatalities.To address this issue, this paper presents a comprehensive Driver Drowsiness Detection System that combines machine learning-based software with real-time hardware integration.The proposed system utilizes computer vision techniques to monitor facial features such as eye movement and blink rate through a live camera feed.By analyzing these patterns using a trained machine learning model, the system accurately detects signs of fatigue or inattention.Upon detection, an alert mechanism is triggered through hardware components including a buzzer and display alert message.The integration of Python-based image processing with embedded hardware ensures timely warnings, aiming to prevent potential accidents.The system is tested under various real-world conditions to evaluate its responsiveness, accuracy, and reliability.This dual-layer approach enhances road safety by providing a cost-effective, real-time monitoring solution for drowsy driving.