Real-Time Traffic Sign Detection and Recognition using CNN
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/TLA.2020.9082723 http://hdl.handle.net/11449/201734 |
Resumo: | Traffic signs presents on streets and highways have a distinct set of features which may be used to differentiate each one from each other. We propose in this paper a real-time traffic sign detection and recognition algorithm using neural networks. In order to detect traffic sign we used a Faster R-CNN (Region-Based Convolutional Neural Network), and to classify we used a Convolutional Neural Network using two different architectures. Some factors can make it difficult, such as light, occlusion, blurring, and others. This work can be applied in several areas, such as Advanced Driving Assistant System and autonomous cars. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Real-Time Traffic Sign Detection and Recognition using CNNComputer VisionConvolutional Neural NetworkRegion-Based Convolutional Neural NetworkTraffic signs presents on streets and highways have a distinct set of features which may be used to differentiate each one from each other. We propose in this paper a real-time traffic sign detection and recognition algorithm using neural networks. In order to detect traffic sign we used a Faster R-CNN (Region-Based Convolutional Neural Network), and to classify we used a Convolutional Neural Network using two different architectures. Some factors can make it difficult, such as light, occlusion, blurring, and others. This work can be applied in several areas, such as Advanced Driving Assistant System and autonomous cars.Universidade Do Oeste Paulista (Unoeste) Presidente PrudenteUniversidade Estadual Paulista (Unesp) Presidente PrudenteUniversidade de Fortaleza (Unifor)Universidade Estadual Paulista (Unesp) Presidente PrudentePresidente PrudenteUniversidade Estadual Paulista (Unesp)Universidade de Fortaleza (Unifor)Santos, Daniel CastrianiSilva, Francisco Assis DaPereira, Danillo RobertoAlmeida, Leandro Luiz DeArtero, Almir Olivette [UNESP]Piteri, Marco Antonio [UNESP]Albuquerque, Victor Hugo2020-12-12T02:40:27Z2020-12-12T02:40:27Z2020-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article522-529http://dx.doi.org/10.1109/TLA.2020.9082723IEEE Latin America Transactions, v. 18, n. 3, p. 522-529, 2020.1548-0992http://hdl.handle.net/11449/20173410.1109/TLA.2020.90827232-s2.0-85084288646Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporIEEE Latin America Transactionsinfo:eu-repo/semantics/openAccess2024-06-19T14:32:04Zoai:repositorio.unesp.br:11449/201734Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:39:06.308686Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Real-Time Traffic Sign Detection and Recognition using CNN |
title |
Real-Time Traffic Sign Detection and Recognition using CNN |
spellingShingle |
Real-Time Traffic Sign Detection and Recognition using CNN Santos, Daniel Castriani Computer Vision Convolutional Neural Network Region-Based Convolutional Neural Network |
title_short |
Real-Time Traffic Sign Detection and Recognition using CNN |
title_full |
Real-Time Traffic Sign Detection and Recognition using CNN |
title_fullStr |
Real-Time Traffic Sign Detection and Recognition using CNN |
title_full_unstemmed |
Real-Time Traffic Sign Detection and Recognition using CNN |
title_sort |
Real-Time Traffic Sign Detection and Recognition using CNN |
author |
Santos, Daniel Castriani |
author_facet |
Santos, Daniel Castriani Silva, Francisco Assis Da Pereira, Danillo Roberto Almeida, Leandro Luiz De Artero, Almir Olivette [UNESP] Piteri, Marco Antonio [UNESP] Albuquerque, Victor Hugo |
author_role |
author |
author2 |
Silva, Francisco Assis Da Pereira, Danillo Roberto Almeida, Leandro Luiz De Artero, Almir Olivette [UNESP] Piteri, Marco Antonio [UNESP] Albuquerque, Victor Hugo |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Presidente Prudente Universidade Estadual Paulista (Unesp) Universidade de Fortaleza (Unifor) |
dc.contributor.author.fl_str_mv |
Santos, Daniel Castriani Silva, Francisco Assis Da Pereira, Danillo Roberto Almeida, Leandro Luiz De Artero, Almir Olivette [UNESP] Piteri, Marco Antonio [UNESP] Albuquerque, Victor Hugo |
dc.subject.por.fl_str_mv |
Computer Vision Convolutional Neural Network Region-Based Convolutional Neural Network |
topic |
Computer Vision Convolutional Neural Network Region-Based Convolutional Neural Network |
description |
Traffic signs presents on streets and highways have a distinct set of features which may be used to differentiate each one from each other. We propose in this paper a real-time traffic sign detection and recognition algorithm using neural networks. In order to detect traffic sign we used a Faster R-CNN (Region-Based Convolutional Neural Network), and to classify we used a Convolutional Neural Network using two different architectures. Some factors can make it difficult, such as light, occlusion, blurring, and others. This work can be applied in several areas, such as Advanced Driving Assistant System and autonomous cars. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:40:27Z 2020-12-12T02:40:27Z 2020-03-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/TLA.2020.9082723 IEEE Latin America Transactions, v. 18, n. 3, p. 522-529, 2020. 1548-0992 http://hdl.handle.net/11449/201734 10.1109/TLA.2020.9082723 2-s2.0-85084288646 |
url |
http://dx.doi.org/10.1109/TLA.2020.9082723 http://hdl.handle.net/11449/201734 |
identifier_str_mv |
IEEE Latin America Transactions, v. 18, n. 3, p. 522-529, 2020. 1548-0992 10.1109/TLA.2020.9082723 2-s2.0-85084288646 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
IEEE Latin America Transactions |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
522-529 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128959960842240 |