American Journal of Artificial Intelligence

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Real-Time Distracted Drivers Detection Using Deep Learning

Received: 22 February 2019    Accepted: 08 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%.

DOI 10.11648/j.ajai.20190301.11
Published in American Journal of Artificial Intelligence (Volume 3, Issue 1, June 2019)
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), 2024. Published by Science Publishing Group

Keywords

Distracted Driver, CNN, Deep Learning, Activation Functions

References
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Author Information
  • Department of Mechatronics, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

  • Department of Mechatronics, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Cite This Article
  • 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://download.sciencepg.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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    T1  - Real-Time Distracted Drivers Detection Using Deep Learning
    AU  - Vlad Tamas
    AU  - Vistrian Maties
    Y1  - 2019/05/15
    PY  - 2019
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    JF  - American Journal of Artificial Intelligence
    JO  - American Journal of Artificial Intelligence
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    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
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