Fuji-SfM dataset

The Fuji-SfM dataset includes: (1) a set of 288 colour images and the corresponding annotations (apples segmentation masks) for training instance segmentation neural networks such as Mask-RCNN; (2) a set of 582 images defining a motion sequence of the scene which was used to generate the 3D model of...

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Authors: Gené Mola, Jordi, Sanz Cortiella, Ricardo, Rosell Polo, Joan Ramon, Morros Rubió, Josep Ramon, Ruiz Hidalgo, Javier, Vilaplana Besler, Verónica, Gregorio López, Eduard
Format: other
Publication Date:2020
Country:España
Institution:Universitat de Lleida (UdL)
Repository:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/68505
Online Access:http://doi.org/10.5281/zenodo.3712808
https://doi.org/10.5281/zenodo.3712808
http://hdl.handle.net/10459.1/68505
Access Level:Open access
Keyword:Fruit detection
Structure-from-motion (SfM)
Photogrammetry
Terrestrial remote sensing
Agrobotics
Yield Prediction
Yield Mapping
Artificial Intelligence
Computer Vision
Instanse segmentation
Deep Learning
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repository_id_str
spelling Fuji-SfM datasetGené Mola, JordiSanz Cortiella, RicardoRosell Polo, Joan RamonMorros Rubió, Josep RamonRuiz Hidalgo, JavierVilaplana Besler, VerónicaGregorio López, EduardFruit detectionStructure-from-motion (SfM)PhotogrammetryTerrestrial remote sensingAgroboticsYield PredictionYield MappingArtificial IntelligenceComputer VisionInstanse segmentationDeep LearningThe Fuji-SfM dataset includes: (1) a set of 288 colour images and the corresponding annotations (apples segmentation masks) for training instance segmentation neural networks such as Mask-RCNN; (2) a set of 582 images defining a motion sequence of the scene which was used to generate the 3D model of 11 Fuji apple trees containing 1455 apples by using SfM; (3) the 3D point cloud of the scanned scene with the corresponding apple positions ground truth in global coordinates. This data allows the development, training, and test of fruit detection algorithms either based on RGB images, on coloured point clouds or on the combination of both types of data.Dades primàries associades a un article publicat a la revista Computers and Electronics in Agriculture disponible a l'adreça https://doi.org/10.1016/j.compag.2019.105165 i a la revista Data in Brief disponible a l'adreça https://doi.org/10.1016/j.dib.2020.105591Universitat de Lleida2020info:eu-repo/semantics/otherinfo:eu-repo/semantics/datasethttp://doi.org/10.5281/zenodo.3712808https://doi.org/10.5281/zenodo.3712808http://hdl.handle.net/10459.1/68505reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésMINECO/PN2013-2016/AGL2013-48297-C2-2-RMINECO/PN2013-2016/RTI2018-094222-B-I00MINECO/PN2013-2016/TEC2016-75976-Rhttp://hdl.handle.net/10459.1/67802http://hdl.handle.net/10459.1/68506https://hdl.handle.net/10803/669110cc-by (c) Jordi Gené et al., 2020info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/685052026-06-24T12:42:17Z
dc.title.none.fl_str_mv Fuji-SfM dataset
title Fuji-SfM dataset
spellingShingle Fuji-SfM dataset
Gené Mola, Jordi
Fruit detection
Structure-from-motion (SfM)
Photogrammetry
Terrestrial remote sensing
Agrobotics
Yield Prediction
Yield Mapping
Artificial Intelligence
Computer Vision
Instanse segmentation
Deep Learning
title_short Fuji-SfM dataset
title_full Fuji-SfM dataset
title_fullStr Fuji-SfM dataset
title_full_unstemmed Fuji-SfM dataset
title_sort Fuji-SfM dataset
dc.creator.none.fl_str_mv Gené Mola, Jordi
Sanz Cortiella, Ricardo
Rosell Polo, Joan Ramon
Morros Rubió, Josep Ramon
Ruiz Hidalgo, Javier
Vilaplana Besler, Verónica
Gregorio López, Eduard
author Gené Mola, Jordi
author_facet Gené Mola, Jordi
Sanz Cortiella, Ricardo
Rosell Polo, Joan Ramon
Morros Rubió, Josep Ramon
Ruiz Hidalgo, Javier
Vilaplana Besler, Verónica
Gregorio López, Eduard
author_role author
author2 Sanz Cortiella, Ricardo
Rosell Polo, Joan Ramon
Morros Rubió, Josep Ramon
Ruiz Hidalgo, Javier
Vilaplana Besler, Verónica
Gregorio López, Eduard
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Fruit detection
Structure-from-motion (SfM)
Photogrammetry
Terrestrial remote sensing
Agrobotics
Yield Prediction
Yield Mapping
Artificial Intelligence
Computer Vision
Instanse segmentation
Deep Learning
topic Fruit detection
Structure-from-motion (SfM)
Photogrammetry
Terrestrial remote sensing
Agrobotics
Yield Prediction
Yield Mapping
Artificial Intelligence
Computer Vision
Instanse segmentation
Deep Learning
description The Fuji-SfM dataset includes: (1) a set of 288 colour images and the corresponding annotations (apples segmentation masks) for training instance segmentation neural networks such as Mask-RCNN; (2) a set of 582 images defining a motion sequence of the scene which was used to generate the 3D model of 11 Fuji apple trees containing 1455 apples by using SfM; (3) the 3D point cloud of the scanned scene with the corresponding apple positions ground truth in global coordinates. This data allows the development, training, and test of fruit detection algorithms either based on RGB images, on coloured point clouds or on the combination of both types of data.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/other
info:eu-repo/semantics/dataset
format other
dc.identifier.none.fl_str_mv http://doi.org/10.5281/zenodo.3712808
https://doi.org/10.5281/zenodo.3712808
http://hdl.handle.net/10459.1/68505
url http://doi.org/10.5281/zenodo.3712808
https://doi.org/10.5281/zenodo.3712808
http://hdl.handle.net/10459.1/68505
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv MINECO/PN2013-2016/AGL2013-48297-C2-2-R
MINECO/PN2013-2016/RTI2018-094222-B-I00
MINECO/PN2013-2016/TEC2016-75976-R
http://hdl.handle.net/10459.1/67802
http://hdl.handle.net/10459.1/68506
https://hdl.handle.net/10803/669110
dc.rights.none.fl_str_mv cc-by (c) Jordi Gené et al., 2020
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Jordi Gené et al., 2020
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universitat de Lleida
publisher.none.fl_str_mv Universitat de Lleida
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_ 1869420207614722048
score 15.811543

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