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Cycle Route Signs Detection Using Deep Learning

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


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.


  • 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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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).

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Correspondence to Michal Dobrovolny .

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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.

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