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dc.contributor.authorMivalt, Filip
dc.contributor.authorNejedly, Petr
dc.date.accessioned2019-03-04T10:05:41Z
dc.date.available2019-03-04T10:05:41Z
dc.date.issued2018cs
dc.identifier.citationProceedings of the 24th Conference STUDENT EEICT 2018. s. 188-190. ISBN 978-80-214-5614-3cs
dc.identifier.isbn978-80-214-5614-3
dc.identifier.urihttp://hdl.handle.net/11012/138209
dc.description.abstractThe 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.en
dc.formattextcs
dc.format.extent188-190cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 24th Conference STUDENT EEICT 2018en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectMachine learningen
dc.subjectConvolutional neural networksen
dc.subjectTraffic signs recognitionen
dc.titleClassification Of Traffic Signs By Convolutional Neural Networksen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date26.04.2018cs
but.event.titleStudent EEICT 2018cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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