Real-Time Traffic Sign Detection and Recognition using CNN
dc.contributor.author | Santos, Daniel Castriani | |
dc.contributor.author | Silva, Francisco Assis Da | |
dc.contributor.author | Pereira, Danillo Roberto | |
dc.contributor.author | Almeida, Leandro Luiz De | |
dc.contributor.author | Artero, Almir Olivette [UNESP] | |
dc.contributor.author | Piteri, Marco Antonio [UNESP] | |
dc.contributor.author | Albuquerque, Victor Hugo | |
dc.contributor.institution | Presidente Prudente | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.contributor.institution | Universidade de Fortaleza (Unifor) | |
dc.date.accessioned | 2020-12-12T02:40:27Z | |
dc.date.available | 2020-12-12T02:40:27Z | |
dc.date.issued | 2020-03-01 | |
dc.description.abstract | 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. | en |
dc.description.affiliation | Universidade Do Oeste Paulista (Unoeste) Presidente Prudente | |
dc.description.affiliation | Universidade Estadual Paulista (Unesp) Presidente Prudente | |
dc.description.affiliation | Universidade de Fortaleza (Unifor) | |
dc.description.affiliationUnesp | Universidade Estadual Paulista (Unesp) Presidente Prudente | |
dc.format.extent | 522-529 | |
dc.identifier | http://dx.doi.org/10.1109/TLA.2020.9082723 | |
dc.identifier.citation | IEEE Latin America Transactions, v. 18, n. 3, p. 522-529, 2020. | |
dc.identifier.doi | 10.1109/TLA.2020.9082723 | |
dc.identifier.issn | 1548-0992 | |
dc.identifier.scopus | 2-s2.0-85084288646 | |
dc.identifier.uri | http://hdl.handle.net/11449/201734 | |
dc.language.iso | por | |
dc.relation.ispartof | IEEE Latin America Transactions | |
dc.source | Scopus | |
dc.subject | Computer Vision | |
dc.subject | Convolutional Neural Network | |
dc.subject | Region-Based Convolutional Neural Network | |
dc.title | Real-Time Traffic Sign Detection and Recognition using CNN | en |
dc.type | Artigo | |
unesp.author.orcid | 0000-0003-4994-9914[2] | |
unesp.author.orcid | 0000-0001-7934-6482[3] | |
unesp.author.orcid | 0000-0003-3293-7386[4] | |
unesp.author.orcid | 0000-0001-6824-7251[5] | |
unesp.author.orcid | 0000-0003-3886-4309[7] | |
unesp.department | Matemática e Computação - FCT | pt |