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Driver fatigue and drowsiness are major causes of road accidents worldwide, emphasizing the need for an efficient and reliable detection system. This paper presents a Drowsiness Detection System based on Convolutional Neural Networks (CNNs), designed to monitor driver alertness in real time. The proposed system captures facial images using a camera and analyzes visual features such as eye closure, yawning frequency, and head position. A CNN-based model is trained to classify the driver’s state as active or drowsy by learning spatial and temporal patterns from the image dataset. The system generates alerts through visual or audio notifications when signs of fatigue are detected, helping to prevent potential accidents. Experimental evaluation demonstrates high accuracy and robustness of the proposed model under varying lighting and environmental conditions. The integration of deep learning with computer vision provides a cost-effective and automated solution for enhancing road safety and driver assistance technologies.
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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: YEGIREDDI RAMESH, rameshyegireddi@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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