Real-Time Distracted Drivers Detection Using Deep Learning

Vlad Tamas; Vistrian Maties

doi:doi:10.11648/j.ajai.20190301.11

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Real-Time Distracted Drivers Detection Using Deep Learning

Vlad Tamas, Vistrian Maties

Published in American Journal of Artificial Intelligence (Volume 3, Issue 1)

Received: 22 February 2019 Accepted: 8 April 2019 Published: 15 May 2019

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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%.

Published in	American Journal of Artificial Intelligence (Volume 3, Issue 1)
DOI	10.11648/j.ajai.20190301.11
Page(s)	1-8
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Distracted Driver, CNN, Deep Learning, Activation Functions

References

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APA Style

Vlad Tamas, Vistrian Maties. (2019). Real-Time Distracted Drivers Detection Using Deep Learning. American Journal of Artificial Intelligence, 3(1), 1-8. https://doi.org/10.11648/j.ajai.20190301.11

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ACS Style

Vlad Tamas; Vistrian Maties. Real-Time Distracted Drivers Detection Using Deep Learning. Am. J. Artif. Intell. 2019, 3(1), 1-8. doi: 10.11648/j.ajai.20190301.11

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AMA Style

Vlad Tamas, Vistrian Maties. Real-Time Distracted Drivers Detection Using Deep Learning. Am J Artif Intell. 2019;3(1):1-8. doi: 10.11648/j.ajai.20190301.11

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@article{10.11648/j.ajai.20190301.11,
  author = {Vlad Tamas and Vistrian Maties},
  title = {Real-Time Distracted Drivers Detection Using Deep Learning},
  journal = {American Journal of Artificial Intelligence},
  volume = {3},
  number = {1},
  pages = {1-8},
  doi = {10.11648/j.ajai.20190301.11},
  url = {https://doi.org/10.11648/j.ajai.20190301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20190301.11},
  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%.},
 year = {2019}
}

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TY  - JOUR
T1  - Real-Time Distracted Drivers Detection Using Deep Learning
AU  - Vlad Tamas
AU  - Vistrian Maties
Y1  - 2019/05/15
PY  - 2019
N1  - https://doi.org/10.11648/j.ajai.20190301.11
DO  - 10.11648/j.ajai.20190301.11
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 1
EP  - 8
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20190301.11
AB  - 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%.
VL  - 3
IS  - 1
ER  -

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Author Information

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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APA Style

Vlad Tamas, Vistrian Maties. (2019). Real-Time Distracted Drivers Detection Using Deep Learning. American Journal of Artificial Intelligence, 3(1), 1-8. https://doi.org/10.11648/j.ajai.20190301.11

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ACS Style

Vlad Tamas; Vistrian Maties. Real-Time Distracted Drivers Detection Using Deep Learning. Am. J. Artif. Intell. 2019, 3(1), 1-8. doi: 10.11648/j.ajai.20190301.11

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AMA Style

Vlad Tamas, Vistrian Maties. Real-Time Distracted Drivers Detection Using Deep Learning. Am J Artif Intell. 2019;3(1):1-8. doi: 10.11648/j.ajai.20190301.11

Copy | Download

@article{10.11648/j.ajai.20190301.11,
  author = {Vlad Tamas and Vistrian Maties},
  title = {Real-Time Distracted Drivers Detection Using Deep Learning},
  journal = {American Journal of Artificial Intelligence},
  volume = {3},
  number = {1},
  pages = {1-8},
  doi = {10.11648/j.ajai.20190301.11},
  url = {https://doi.org/10.11648/j.ajai.20190301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20190301.11},
  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%.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Real-Time Distracted Drivers Detection Using Deep Learning
AU  - Vlad Tamas
AU  - Vistrian Maties
Y1  - 2019/05/15
PY  - 2019
N1  - https://doi.org/10.11648/j.ajai.20190301.11
DO  - 10.11648/j.ajai.20190301.11
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 1
EP  - 8
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20190301.11
AB  - 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%.
VL  - 3
IS  - 1
ER  -

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