Kopecky, Lukas and Dobrovolny, Michal and Fuchs, Antonin and Selamat, Ali and Krejcar, Ondrej (2022) Cycle route signs detection using deep learning. In: Computational Collective Intelligence 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28–30, 2022, Proceedings. Lecture Notes in Computer Science, 1 (NA). Springer Science and Business Media Deutschland GmbH, Cham, Switzerland, pp. 82-94. ISBN 978-303116013-4
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Official URL: http://dx.doi.org/10.1007/978-3-031-16014-1_8
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.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | Computer vision, Machine learning, Object detection, OCR, YOLO, YOLOv5 |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Malaysia-Japan International Institute of Technology |
| ID Code: | 100516 |
| Deposited By: | Widya Wahid |
| Deposited On: | 14 Apr 2023 02:41 |
| Last Modified: | 14 Apr 2023 02:41 |
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