Driver Drowsiness Detection using Deep Learning

Registration ID: IJNRD_222399

Published ID: IJNRD2405627

DOI: Click Here to Get

Ankul kumar , Abhishek Ranjan , Adarsh Raj

Convolutional Neural Network (CNN), Electrooculography (EOG), and Rectified linear activation function (ReLU).

According to the National Highway Traffic Safety Administration (NHTSA), drowsiness is a leading cause of road accidents. To address this problem, various methods based on monitoring driver behavior and vehicle dynamics have been suggested and implemented. Traditional vehicle-based methods typically rely on fixed parameters, such as variations in steering wheel angle or lane departure, to detect drowsiness. However, these parameters may not always accurately indicate a driver's alertness level. Consequently, it is crucial to develop a more effective method for detecting driver drowsiness. Deep learning techniques, particularly convolutional neural networks (CNN), offer a robust solution for identifying drowsiness by analyzing drivers' facial features. The proposed CNN-based approach focuses on the eye and mouth regions, using the nose as a reference point. Utilizing the rectified linear activation function (ReLU), this CNN method achieves an accuracy of 94.95%, outperforming existing methods even under challenging conditions such as low light, different head angles, and drivers wearing transparent glasses.

Published Paper   E-Certificate

Ankul kumar, Abhishek Ranjan, & Adarsh Raj (May-2024). Driver Drowsiness Detection using Deep Learning . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), g214-g222. https://ijnrd.org/papers/IJNRD2405627.pdf

Volume 9 Issue 5, May-2024

Pages : g214-g222

Paper Reg. ID: IJNRD_222399

Published Paper Id: IJNRD2405627

Downloads: 000121992

Research Area: Computer Science & Technology 

Country: Greater noida, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405627.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405627

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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