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International Conference on Computational Collective Intelligence

ICCCI 2022: Computational Collective Intelligence pp 82–94Cite as

Cycle Route Signs Detection Using Deep Learning

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 13501)

Abstract

This article addresses the issue of detecting traffic signs signalling cycle routes. It is also necessary to read the number or text of the cycle route from the given image. These tags are kept under the identifier IS21 and have a defined, uniform design with text in the middle of the tag. The detection was solved using the You Look Only Once (YOLO) model, which works on the principle of a convolutional neural network. The OCR tool PythonOCR was used to read characters from tags. The success rate of IS21 tag detection is 93.4%, and the success rate of reading text from tags is equal to 85.9%. The architecture described in the article is suitable for solving the defined problem.

Keywords

  • YOLOv5
  • YOLO
  • OCR
  • Object detection
  • Machine learning
  • Computer vision

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  • DOI: 10.1007/978-3-031-16014-1_8
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Acknowledgements

The work and the contribution were supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-SPEV-2022-2102).

Author information

Authors and Affiliations

  1. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Hradec Kralove, Czech Republic

    Lukas Kopecky, Michal Dobrovolny, Ali Selamat & Ondrej Krejcar

  2. Rubi Consulting s.r.o., Zámocká 3, Bratislava, Slovakia

    Antonin Fuchs

  3. Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Ali Selamat

  4. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Malaysia

    Ali Selamat

Authors
  1. Lukas Kopecky
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  2. Michal Dobrovolny
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  3. Antonin Fuchs
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  4. Ali Selamat
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  5. Ondrej Krejcar
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Corresponding author

Correspondence to Michal Dobrovolny .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  3. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Kopecky, L., Dobrovolny, M., Fuchs, A., Selamat, A., Krejcar, O. (2022). Cycle Route Signs Detection Using Deep Learning. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-16014-1_8

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