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Spatio-temporal road detection from aerial imagery using CNNs
dc.contributor.author | Luque, Belen |
dc.contributor.author | Morros Rubió, Josep Ramon |
dc.contributor.author | Ruiz Hidalgo, Javier |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2017-05-22T16:13:24Z |
dc.date.available | 2017-05-22T16:13:24Z |
dc.date.issued | 2017 |
dc.identifier.citation | Luque, B., Morros, J.R., Ruiz-Hidalgo, J. Spatio-temporal road detection from aerial imagery using CNNs. A: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. "Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 4: VISAPP". Porto: SCITEPRESS, 2017, p. 493-500. |
dc.identifier.isbn | 978-989-758-225-7 |
dc.identifier.uri | http://hdl.handle.net/2117/104723 |
dc.description.abstract | The main goal of this paper is to detect roads from aerial imagery recorded by drones. To achieve this, we propose a modification of SegNet, a deep fully convolutional neural network for image segmentation. In order to train this neural network, we have put together a database containing videos of roads from the point of view of a small commercial drone. Additionally, we have developed an image annotation tool based on the watershed technique, in order to perform a semi-automatic labeling of the videos in this database. The experimental results using our modified version of SegNet show a big improvement on the performance of the neural network when using aerial imagery, obtaining over 90% accuracy. |
dc.format.extent | 8 p. |
dc.language.iso | eng |
dc.publisher | SCITEPRESS |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
dc.subject.lcsh | Robot vision |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Drone aircraft |
dc.title | Spatio-temporal road detection from aerial imagery using CNNs |
dc.type | Conference report |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Avions no tripulats |
dc.subject.lemac | Visió artificial (Robòtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo |
dc.identifier.doi | 10.5220/0006128904930500 |
dc.relation.publisherversion | http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=Ki5fKyigC7Q=&t=1 |
dc.rights.access | Open Access |
local.identifier.drac | 20337268 |
dc.description.version | Postprint (published version) |
local.citation.author | Luque, B.; Morros, J.R.; Ruiz-Hidalgo, J. |
local.citation.contributor | International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
local.citation.pubplace | Porto |
local.citation.publicationName | Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 4: VISAPP |
local.citation.startingPage | 493 |
local.citation.endingPage | 500 |