Real-Time Driver Drowsiness Detection System for Enhanced Road Safety

This project addresses the critical issue of driver drowsiness, a leading cause of road accidents worldwide. It aims to design a non-intrusive system that detects drowsiness in real-time by analyzing facial features such as eye closure, yawning, and head position using machine learning techniques. A deep learning model was trained and implemented on an embedded system, integrated with a camera and an alert mechanism to warn drivers promptly. By developing a cost-effective and reliable solution, the project seeks to enhance road safety and reduce accidents caused by driver fatigue.