Real-Time Distracted Drivers Detection Using Deep Learning
American Journal of Artificial Intelligence
Volume 3, Issue 1, June 2019, Pages: 1-8
Received: Feb. 22, 2019; Accepted: Apr. 8, 2019; Published: May 15, 2019
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Authors
Vlad Tamas, Department of Mechatronics, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Vistrian Maties, Department of Mechatronics, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
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Abstract
In the last few years, the number of road accidents is increasing worldwide. According to the World Health Organization the most common cause behind these accidents is driver’s distraction and in many cases is caused by the use of a mobile phone. An attempt to develop a system for detecting distracted drivers and warn the responsible person against it was done. The system is a CNN based system that detects and identifies the cause of distraction. The base architecture for the CNN is VGG-16 and is modified for this task. Various activation functions (Leaky ReLU, DReLU, SELU) were used in order to investigate performance. Also, the performance of a lightweight attention module (squeeze-and-excitation) was evaluated. Experimental results show that the system outperforms earlier lightweight models in literature achieving an accuracy of 95.82%.
Keywords
Distracted Driver, CNN, Deep Learning, Activation Functions
To cite this article
Vlad Tamas, Vistrian Maties, Real-Time Distracted Drivers Detection Using Deep Learning, American Journal of Artificial Intelligence. Vol. 3, No. 1, 2019, pp. 1-8. doi: 10.11648/j.ajai.20190301.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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