Enhancing Driver Safety : Real-Time Distraction and Drowsiness Detection Using Deep Learning

Abstract

This paper presents a comprehensive study on driver distraction and drowsiness detection using a Deep Learning approach. The primary objective is to develop a robust alert system capable of accurately identifying signs of driver fatigue and distraction to enhance road safety. The research methodology utilizes TensorFlow, a widely used open-source platform for deep learning. Specifically, a pre-trained Convolutional Neural Network (CNN) is fine-tuned to recognize patterns and features in the input data, comprising 10 different activities associated with driver distraction. The model refinement process leverages pre-existing knowledge from the pre-trained network, enhancing predictive accuracy through exposure to large amounts of data. The model's performance is assessed based on its proficiency in correctly classifying various states of driver alertness. The key inference drawn from this study is that deep learning techniques demonstrate effectiveness in real-time detection of driver drowsiness and distraction. The fine-tuned model exhibits remarkable accuracy in identifying diverse distraction activities, showcasing its potential for practical implementation within an alert system. This research underscores the promising potential of Machine Learning, specifically the use of pre-trained models in enhancing road safety by proactively preventing accidents resulting from driver fatigue and distraction.