Driver Drowsiness Detection using CNN

Abstract

Abstract: Driver drowsiness is a significant factor contributing to road accidents worldwide, posing a major threat to public safety. This project presents a robust and efficient approach to detecting driver drowsiness using Convolutional Neural Networks (CNN). The proposed system aims to enhance road safety by monitoring drivers in real-time and providing timely warnings to prevent accidents caused by fatigue. The methodology involves capturing video frames of the driver's face using a camera installed in the vehicle. The CNN model is trained on a comprehensive dataset containing images of alert and drowsy states. Key facial landmarks and features, such as eye closure duration, yawning frequency, and head position, are extracted and analyzed to determine the driver's level of alertness. By leveraging the powerful feature extraction capabilities of CNNs, the system can accurately distinguish between alert and drowsy states with high precision. Experimental results demonstrate the effectiveness of the proposed system in real-world scenarios, achieving a high accuracy rate in detecting drowsiness. The system's performance is evaluated against various benchmarks, showcasing its potential for integration into modern vehicles as a preventive safety measure. The deployment of this CNN-based drowsiness detection system can significantly reduce the risk of accidents, contributing to safer driving conditions and saving lives.