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.
