Driver Drowsiness Detection using CNN

Abstract

Abstract: This research introduces a Driver Drowsiness Detection system employing Convolutional Neural Networks (CNN). The system analyses real-time facial features from in-vehicle cameras to determine a driver's alertness level. Trained on diverse datasets, the CNN model demonstrates high accuracy in identifying drowsiness signs, making it suitable for real-world deployment. This system contributes to road safety by providing timely alerts to prevent accidents caused by driver fatigue. As road safety remains a critical concern, the development of intelligent systems to mitigate driver-related risks has become imperative. Driver drowsiness is a major factor contributing to road accidents, emphasizing the need for robust and real-time detection mechanisms. This research presents a novel approach for Driver Drowsiness Detection using Convolutional Neural Networks (CNN). The proposed system utilizes CNN architecture to analyse facial features extracted from real-time video streams captured by an in-vehicle camera. Facial landmarks and expressions are processed to determine the driver's level of alertness. The CNN model is trained on a diverse dataset comprising both drowsy and alert facial expressions, ensuring its adaptability to different driving conditions and individual characteristics. The system's effectiveness is evaluated through extensive experiments using various datasets and scenarios. The results demonstrate the CNN's capability to accurately identify signs of driver drowsiness, achieving high precision and recall rates. Furthermore, the model's real-time processing capabilities make it suitable for deployment in practical, on-road scenarios. The proposed Driver Drowsiness Detection system presents a promising solution to enhance road safety by providing timely alerts to drivers when signs of drowsiness are detected. This research contributes to the ongoing efforts to integrate artificial intelligence into vehicle safety systems, ultimately reducing the risk of accidents caused by driver fatigue.