Article Per Year (5 Year)
p-Index From 2021 - 2026
0.339
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Md. Faisal Bin Abdul Aziz
Comilla University
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Convolutional neural network-based real-time drowsy driver detection for accident prevention Nippon Datta; Tanjim Mahmud; Manoara Begum; Mohammad Tarek Aziz; Dilshad Islam; Md. Faisal Bin Abdul Aziz; Khudaybergen Kochkarov; Temur Eshchanov; Valisher Sapayev Odilbek Uglu; Sobir Parmanov; Mohammad Shahadat Hossain; Karl Andersson
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26059

Abstract

Drowsy driving significantly threatens road safety, contributing to many accidents globally. This paper presents a convolutional neural network (CNN)-based real-time drowsy driver detection system aimed at preventing such accidents, particularly for deployment in Android applications. We propose a lightweight CNN architecture that effectively identifies drowsiness and microsleep episodes by categorizing driver facial expressions into four distinct categories: close-eye expressions, open-eye expressions, yawns, and no yawns. Our model, which employs facial landmark detection and various pre-processing techniques to enhance accuracy, achieves an impressive 96.6% accuracy. This performance surpasses several popular CNN architectures, including VGG16, VGG19, MobileNetV2, ResNet50, and DenseNet121. Notably, our proposed model is highly efficient, with only 0.4 million parameters and a memory requirement of 1.51 MB, making it ideal for real-time applications. The comparative analysis highlights the superior balance between accuracy and resource efficiency of our model, demonstrating its potential for practical deployment in reducing accidents caused by driver fatigue.
Co-Authors Dilshad Islam Karl Andersson Khudaybergen Kochkarov Manoara Begum Mohammad Shahadat Hossain Mohammad Tarek Aziz Nippon Datta Sobir Parmanov Tanjim Mahmud Temur Eshchanov Valisher Sapayev Odilbek Uglu