dc.creator | Arcos García, Álvaro | es |
dc.creator | Soilán, Mario | es |
dc.creator | Álvarez García, Juan Antonio | es |
dc.creator | Riveiro, Belén | es |
dc.date.accessioned | 2021-09-13T09:35:51Z | |
dc.date.available | 2021-09-13T09:35:51Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Arcos García, Á., Soilán, M., Álvarez García, J.A. y Riveiro, B. (2017). Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems. Expert Systems With Applications, 89, 286-295. | |
dc.identifier.issn | 0957-4174 | es |
dc.identifier.uri | https://hdl.handle.net/11441/125642 | |
dc.description.abstract | This paper presents an efficient two-stage traffic sign recognition system. First, 3D point cloud data is
acquired by a LINX Mobile Mapper system and processed to automatically detect traffic signs based on
their retro-reflective material. Then, classification is carried out over the point cloud projection on RGB
images applying a Deep Neural Network which comprises convolutional and spatial transformer layers.
This network is evaluated in three European traffic sign datasets. On the GTSRB, it outperforms previous
state-of-the-art published works and achieves top-1 rank with an accuracy of 99.71%. Furthermore, a
Spanish traffic sign recognition dataset is released. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2013-46801-C4-1-R | es |
dc.description.sponsorship | Ministerio de Economia y Competitividad TIN2013-46801-C4-4-R | es |
dc.format | application/pdf | es |
dc.format.extent | 9 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Expert Systems With Applications, 89, 286-295. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Mobile mapping sensors | es |
dc.subject | Point cloud | es |
dc.subject | Traffic sign | es |
dc.subject | Deep learning | es |
dc.subject | Convolutional neural network | es |
dc.subject | Spatial transformer network | es |
dc.title | Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2013-46801-C4-1-R | es |
dc.relation.projectID | TIN2013-46801-C4-4-R | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0957417417305195 | es |
dc.identifier.doi | 10.1016/j.eswa.2017.07.042 | es |
dc.contributor.group | Universidad de Sevilla. TIC134: Sistemas Informáticos | es |
dc.journaltitle | Expert Systems With Applications | es |
dc.publication.volumen | 89 | es |
dc.publication.initialPage | 286 | es |
dc.publication.endPage | 295 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |