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dc.contributor.authorGené Mola, Jordi
dc.contributor.authorVilaplana Besler, Verónica
dc.contributor.authorRosell Polo, Joan Ramon
dc.contributor.authorMorros Rubió, Josep Ramon
dc.contributor.authorRuiz Hidalgo, Javier
dc.contributor.authorGregorio López, Eduard
dc.date.accessioned2019-09-05T13:31:30Z
dc.date.available2019-09-05T13:31:30Z
dc.date.issued2019
dc.identifier.issn2352-3409
dc.identifier.urihttp://hdl.handle.net/10459.1/66667
dc.description.abstractThis article contains data related to the research article entitle 'Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities' [1]. The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. RGB-D sensors have shown potential for fruit detection and localization since they provide 3D information with color data. However, the lack of substantial datasets is a barrier for exploiting the use of these sensors. This article presents the KFuji RGBDS database which is composed by 967 multi-modal images of Fuji apples on trees captured using Microsoft Kinect v2 (Microsoft, Redmond, WA, USA). Each image contains information from 3 different modalities: color (RGB), depth (D) and range corrected IR intensity (S). Ground truth fruit locations were manually annotated, labeling a total of 12,839 apples in all the dataset. The current dataset is publicly available at http://www.grap.udl.cat/publicacions/datasets.html.
dc.description.sponsorshipThis work was partly funded by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya, the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (ERDF) under Grants 2017 SGR 646, AGL2013-48297-C2-2-R and MALEGRA, TEC2016-75976-R. The Spanish Ministry of Education is thanked for Mr. J. Gené’s pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri and Vicens Maquinària Agrícola S.A. for their support during data acquisition.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier Inc.
dc.relationMINECO/PN2013-2016/AGL2013-48297-C2-2-R
dc.relationMINECO/PN2013-2016/TEC2016-75976-R
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1016/j.dib.2019.104289
dc.relation.ispartofData in Brief, 2019, vol. 25, p. 104289
dc.relation.isreferencedbyhttp://hdl.handle.net/10459.1/66484
dc.relation.isreferencedbyhttp://hdl.handle.net/10459.1/68791
dc.rightscc-by (c) Gené et al., 2019
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMulti-modal dataset
dc.subjectfruit detection
dc.subjectDepth cameras
dc.subjectRGB-D cameras
dc.titleKFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2019-09-05T13:31:30Z
dc.identifier.idgrec028820
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.1016/j.dib.2019.104289


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