Cycle Route Signs Detection Using Deep Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019569" target="_blank" >RIV/62690094:18450/22:50019569 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-16014-1_8" target="_blank" >http://dx.doi.org/10.1007/978-3-031-16014-1_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-16014-1_8" target="_blank" >10.1007/978-3-031-16014-1_8</a>
Alternative languages
Result language
angličtina
Original language name
Cycle Route Signs Detection Using Deep Learning
Original language 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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN
978-3-031-16013-4
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
13
Pages from-to
82-94
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Berlín
Event location
Hammamet
Event date
Sep 28, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000871920200008