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Automatic Recognition System for Traffic Signs in Ecuador Based on Faster R-CNN with ZFNet

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Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1675))

Abstract

This research presents an application of the Deep Learning technology in the development of an automatic system detection of traffic signs of Ecuador. The development of this work has been divided into two parts, i) in first a database was built with regulatory and preventive traffic signs, taken in urban environments from several cities in Ecuador. The dataset consists of 52 classes, collected in the various lighting environments (dawn, day, sunset and cloudy) from 6 am to 7 pm, in various localities of Ecuador, ii) then, an object detector based on Faster-RCNN with ZF-Net was implemented as a detection/recognition module. The entire experimental part was developed on the ViiA technology platform, which consists of a vehicle for the implementation of driving assistance systems using Computer Vision and Artificial Intelligence, in real road driving conditions.

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Acknowledgment

This work was supported by I &H Tech, through the direct funding, the electronic equipment, the database and the vehicle for the development of the experiments. Also, we thank the reviewers and editor for their helpful comments.

Author information

Authors and Affiliations

  1. Department of Computer Science and Industry, Universidad Católica del Maule, Talca, Chile

    David Zabala-Blanco

  2. Grupo de investigación COESVI, Facultad de Ingeniería Civil y Mecánica, Universidad Técnica de Ambato campus Huachi, Av. Los Chasquis y Calle Río Payamino, Ambato, Tungurahua, Ecuador

    Milton Aldás

  3. Universidad de las Fuerzas Armadas-ESPE, Av. Gral. Rumiñahui, s/n, PBX 171-5-231B, Sangolquí, Pichincha, Ecuador

    Wilson Román & Marco Flores-Calero

  4. Department of Intelligent Systems, I &H Tech, Latacunga, Cotopaxi, Ecuador

    Joselyn Gallegos & Marco Flores-Calero

Authors
  1. David Zabala-Blanco
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  2. Milton Aldás
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  3. Wilson Román
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  4. Joselyn Gallegos
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  5. Marco Flores-Calero
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Contributions

All the authors have contributed to the development of the work, each one from their expertise.

Corresponding author

Correspondence to Marco Flores-Calero .

Editor information

Editors and Affiliations

  1. Universidad Estatal Península de Santa, La Libertad, Ecuador

    Teresa Guarda

  2. University of Minho, Guimarães, Portugal

    Filipe Portela

  3. BITrum Research Group, Leon, Spain

    Maria Fernanda Augusto

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The authors declare that there is no conflict of interest.

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Zabala-Blanco, D., Aldás, M., Román, W., Gallegos, J., Flores-Calero, M. (2022). Automatic Recognition System for Traffic Signs in Ecuador Based on Faster R-CNN with ZFNet. In: Guarda, T., Portela, F., Augusto, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2022. Communications in Computer and Information Science, vol 1675. Springer, Cham. https://doi.org/10.1007/978-3-031-20319-0_4

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  • DOI: https://doi.org/10.1007/978-3-031-20319-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20318-3

  • Online ISBN: 978-3-031-20319-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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