KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data

This 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...

Descripción completa

Detalles Bibliográficos
Autores: Gené Mola, Jordi, Vilaplana Besler, Verónica, Rosell Polo, Joan Ramon, Morros Rubió, Josep Ramon, Ruiz Hidalgo, Javier, Gregorio López, Eduard
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/66667
Acceso en línea:https://doi.org/10.1016/j.dib.2019.104289
http://hdl.handle.net/10459.1/66667
Access Level:acceso abierto
Palabra clave:Multi-modal dataset
fruit detection
Depth cameras
RGB-D cameras
id ES_ad8d22046505faaa8e9ef2cfed42cb79
oai_identifier_str oai:repositori.udl.cat:10459.1/66667
network_acronym_str ES
network_name_str España
repository_id_str
spelling KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR dataGené Mola, JordiVilaplana Besler, VerónicaRosell Polo, Joan RamonMorros Rubió, Josep RamonRuiz Hidalgo, JavierGregorio López, EduardMulti-modal datasetfruit detectionDepth camerasRGB-D camerasThis 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.This 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.Elsevier2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.1016/j.dib.2019.104289http://hdl.handle.net/10459.1/66667reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/MINECO//AGL2013-48297-C2-2-Rinfo:eu-repo/grantAgreement/MINECO//TEC2016-75976-RReproducció del document publicat a: https://doi.org/10.1016/j.dib.2019.104289Data in Brief, 2019, vol. 25, p. 104289http://hdl.handle.net/10459.1/66484http://hdl.handle.net/10459.1/68791cc-by (c) Gené et al., 2019info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/666672026-06-24T12:42:17Z
dc.title.none.fl_str_mv KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
title KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
spellingShingle KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
Gené Mola, Jordi
Multi-modal dataset
fruit detection
Depth cameras
RGB-D cameras
title_short KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
title_full KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
title_fullStr KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
title_full_unstemmed KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
title_sort KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
dc.creator.none.fl_str_mv Gené Mola, Jordi
Vilaplana Besler, Verónica
Rosell Polo, Joan Ramon
Morros Rubió, Josep Ramon
Ruiz Hidalgo, Javier
Gregorio López, Eduard
author Gené Mola, Jordi
author_facet Gené Mola, Jordi
Vilaplana Besler, Verónica
Rosell Polo, Joan Ramon
Morros Rubió, Josep Ramon
Ruiz Hidalgo, Javier
Gregorio López, Eduard
author_role author
author2 Vilaplana Besler, Verónica
Rosell Polo, Joan Ramon
Morros Rubió, Josep Ramon
Ruiz Hidalgo, Javier
Gregorio López, Eduard
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Multi-modal dataset
fruit detection
Depth cameras
RGB-D cameras
topic Multi-modal dataset
fruit detection
Depth cameras
RGB-D cameras
description This 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.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.dib.2019.104289
http://hdl.handle.net/10459.1/66667
url https://doi.org/10.1016/j.dib.2019.104289
http://hdl.handle.net/10459.1/66667
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO//AGL2013-48297-C2-2-R
info:eu-repo/grantAgreement/MINECO//TEC2016-75976-R
Reproducció del document publicat a: https://doi.org/10.1016/j.dib.2019.104289
Data in Brief, 2019, vol. 25, p. 104289
http://hdl.handle.net/10459.1/66484
http://hdl.handle.net/10459.1/68791
dc.rights.none.fl_str_mv cc-by (c) Gené et al., 2019
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Gené et al., 2019
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869416467634585600
score 15.812429

Similares en LA Referencia