Convolutional neural network-based real-time drowsy
driver detection for accident prevention

Convolutional neural network-based real-time drowsy
driver detection for accident prevention

Convolutional neural network
Deep learning
Driver
Drowsiness
Lightweight convolutional neural
network

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.

Nippon Datta1, Tanjim Mahmud2, Manoara Begum3, Mohammad Tarek Aziz1, Dilshad Islam4, Md.
Faisal Bin Abdul Aziz5, Khudaybergen Kochkarov6, Temur Eshchanov7, Valisher Sapayev Odilbek
Uglu8, Sobir Parmanov9, Mohammad Shahadat Hossain10,11, Karl Andersson11

Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA

Mar 11, 2025

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