Classification of Traffic Signs by Convolutional Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F18%3APU127920" target="_blank" >RIV/00216305:26220/18:PU127920 - isvavai.cz</a>
Result on the web
<a href="http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf" target="_blank" >http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2018_sbornik.pdf</a>
DOI - Digital Object Identifier
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Result language
čeština
Original language name
Classification of Traffic Signs by Convolutional Neural Networks
Original language description
The paper presented here describes traffic signs classification method based on a convolutional neural network (CNN). The CNN was trained and tested on the public database of German traffic signs with 43 mostly used traffic sign types. Proposed technique achieved overall classification F1 score 89.97 percent on a hidden testing dataset.
Czech name
Classification of Traffic Signs by Convolutional Neural Networks
Czech description
The paper presented here describes traffic signs classification method based on a convolutional neural network (CNN). The CNN was trained and tested on the public database of German traffic signs with 43 mostly used traffic sign types. Proposed technique achieved overall classification F1 score 89.97 percent on a hidden testing dataset.

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 24th Conference STUDENT EEICT 2018
ISBN
978-80-214-5614-3
ISSN
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e-ISSN
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Number of pages
3
Pages from-to
188-190
Publisher name
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních
Place of publication
Brno
Event location
Brno
Event date
Apr 26, 2018
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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