Enhancing Road Safety: Developing a Machine Learning-Based System for Real-Time Drowsiness Detection

Abstract

In today's society, the frequency of accidents brought on by sleepy drivers is a serious and urgent problem. There are various types of sleepiness detection technologies on the market, but given how frequently these accidents occur, more accurate and reliable solutions are needed. This research aims to address this problem by various types of developing a Drowsiness Detection system using real-time image processing and machine learning. The proposed method utilizes publicly accessible various quantity of datasets, which comprise images and videos of drivers with varying various types of levels of attention. Using these preprocessed datasets, a Convolutional Neural Network (CNN) model is trained. In order to provide that more potentially drowsy drivers with timely warnings, the model is designed to detect core thinking of sleepiness indications in real-time. This research is an all-encompassing endeavor to improve road safety by various types of addressing the issue of driver weariness. By utilizing real-time image processing and advanced various types of machine learning algorithms, the proposed system aims to provide a more accurate and more reliable solution for sleepiness detection. The research's conclusions and developments various types of might make roads safer and spare the lives of drivers and pedestrians by lowering the frequency of accidents caused by drowsy driving.