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Convolutional Neural Networks for Traffic Signs Recognition

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Advanced Communication Systems and Information Security ( ACOSIS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1264))

Abstract

The application fields of traffic signs recognition are multiple, including autonomous vehicles, self-driving cars, Advanced Driver Assistance Systems (ADAS), etc. The ultimate goal of these systems is to save lives on roads by reducing the number of accidents. Specifically, the incidents caused by drivers’ distraction and inattention. Efficient traffic signs recognition systems represent then one of the major components in making cars safer, not only for drivers, but for pedestrians as well. In this domain of research, Deep Learning approaches have gained more popularity due to their appealing performances. However, these approaches still face many difficulties, especially, those related to adverse conditions and real time response. Furthermore, these methods are very demanding in terms of hardware requirements, computational load and training data. From this perspective, our work aims to create an efficient Deep Learning model, for the classification of traffic signs, under adverse and challenging conditions. The objective of the research is to reach a high recognition accuracy, via a faster learning process, while using limited data and hardware resources. The obtained results show that CNNs could reach an accuracy of more than 99.6% for signs with no challenges. However, their accuracy decreases when dealing with more challenging traffic signs. The results shows also that lighter architectures are efficient in terms of generalization, accuracy and speed.

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Author information

Authors and Affiliations

  1. Smart Systems Laboratory (SSL), National School of Computer Science and Systems Analysis ENSIAS, Mohammed V University, Rabat, Morocco

    Btissam Bousarhane & Driss Bouzidi

Authors
  1. Btissam Bousarhane
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  2. Driss Bouzidi
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Corresponding author

Correspondence to Btissam Bousarhane .

Editor information

Editors and Affiliations

  1. Mohammed V University, Rabat, Morocco

    Mostafa Belkasmi

  2. Centrale Supélec and Sorbonne University, Paris, France

    Jalel Ben-Othman

  3. Memorial University of Newfoundland, St. John's, NL, Canada

    Cheng Li

  4. Mohammed V University, Rabat, Morocco

    Mohamed Essaaidi

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Bousarhane, B., Bouzidi, D. (2020). Convolutional Neural Networks for Traffic Signs Recognition. In: Belkasmi, M., Ben-Othman, J., Li, C., Essaaidi, M. (eds) Advanced Communication Systems and Information Security. ACOSIS 2019. Communications in Computer and Information Science, vol 1264. Springer, Cham. https://doi.org/10.1007/978-3-030-61143-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-61143-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61142-2

  • Online ISBN: 978-3-030-61143-9

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