A Review on Deep Learning Techniques for Detecting Driver Fatigue

This project proposes an intelligent driver monitoring system designed to enhance road safety by detecting drowsiness and alcohol impairment. It uses visual information to track facial features, focusing on eye and mouth movements, and calculates the Percentage of Eye Closure (PERCLOS) — a proven metric for detecting fatigue. Haar cascade algorithms are employed for real-time detection of eyes and mouth, enabling early identification of fatigue signs like prolonged eye closure and yawning. Deep learning techniques, particularly the Softmax function, are used for accurate classification of drowsiness levels. Additionally, the system integrates alcohol pulse detection to determine driver impairment. By issuing timely alerts, the system aims to reduce accidents caused by fatigue and intoxication, especially among drivers of long-haul and heavy vehicles. .