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Cycle route signs detection using deep learning

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 detec...

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Main Authors:	Kopecky, Lukas, Dobrovolny, Michal, Fuchs, Antonin, Selamat, Ali, Krejcar, Ondrej
格式:	Conference or Workshop Item
出版:	2022
主题:	QA76 Computer software
在线阅读:	http://eprints.utm.my/id/eprint/100513/ http://dx.doi.org/10.1007/978-3-031-16014-1_8
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id	my.utm.100513
record_format	eprints
spelling	my.utm.1005132023-04-14T02:41:01Z http://eprints.utm.my/id/eprint/100513/ Cycle route signs detection using deep learning Kopecky, Lukas Dobrovolny, Michal Fuchs, Antonin Selamat, Ali Krejcar, Ondrej QA76 Computer software 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. 2022 Conference or Workshop Item PeerReviewed Kopecky, Lukas and Dobrovolny, Michal and Fuchs, Antonin and Selamat, Ali and Krejcar, Ondrej (2022) Cycle route signs detection using deep learning. In: 14th International Conference on Computational Collective Intelligence , ICCCI 2022, 28 - 30 September 2022, Hammamet, Tunisia. http://dx.doi.org/10.1007/978-3-031-16014-1_8
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA76 Computer software
spellingShingle	QA76 Computer software Kopecky, Lukas Dobrovolny, Michal Fuchs, Antonin Selamat, Ali Krejcar, Ondrej Cycle route signs detection using deep learning
description	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.
format	Conference or Workshop Item
author	Kopecky, Lukas Dobrovolny, Michal Fuchs, Antonin Selamat, Ali Krejcar, Ondrej
author_facet	Kopecky, Lukas Dobrovolny, Michal Fuchs, Antonin Selamat, Ali Krejcar, Ondrej
author_sort	Kopecky, Lukas
title	Cycle route signs detection using deep learning
title_short	Cycle route signs detection using deep learning
title_full	Cycle route signs detection using deep learning
title_fullStr	Cycle route signs detection using deep learning
title_full_unstemmed	Cycle route signs detection using deep learning
title_sort	cycle route signs detection using deep learning
publishDate	2022
url	http://eprints.utm.my/id/eprint/100513/ http://dx.doi.org/10.1007/978-3-031-16014-1_8
_version_	1764222577579917312
score	13.153044

Cycle route signs detection using deep learning

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