AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation
Refers to: Gené-Mola, J. [et al.]. Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation. "Computers and electronics in agriculture", Juny 2023, vol. 209, article 107854. https://doi.org/10.1016/j.compag.2023.107854 http://hdl.handle.net/2...
| Autores: | , , , , , , , , , , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/400254 |
| Acesso em linha: | https://hdl.handle.net/2117/400254 https://dx.doi.org/10.1016/j.dib.2023.110000 |
| Access Level: | acceso abierto |
| Palavra-chave: | Computer vision Agriculture -- Automation Apples Instance segmentation Modal segmentation Amodal segmentation Yield prediction Depth image Fruit measurement Fruit visibility Agricultural robotics Dataset Visió per ordinador Agricultura -- Automatització Pomes Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Robòtica |
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| dc.title.none.fl_str_mv |
AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation |
| title |
AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation |
| spellingShingle |
AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation Gené Mola, Jordi Computer vision Agriculture -- Automation Apples Instance segmentation Modal segmentation Amodal segmentation Yield prediction Depth image Fruit measurement Fruit visibility Agricultural robotics Dataset Visió per ordinador Agricultura -- Automatització Pomes Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Robòtica |
| title_short |
AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation |
| title_full |
AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation |
| title_fullStr |
AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation |
| title_full_unstemmed |
AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation |
| title_sort |
AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation |
| dc.creator.none.fl_str_mv |
Gené Mola, Jordi Ferrer Ferrer, Mar Hemming, Jochen van Dalfsen, Pieter de Hoog, Dirk Sanz Cortiella, Ricardo Rosell Polo, Joan R. Morros Rubió, Josep Ramon|||0000-0002-1395-487X Vilaplana Besler, Verónica|||0000-0001-6924-9961 Ruiz Hidalgo, Javier|||0000-0001-6774-685X Gregorio López, Eduard |
| author |
Gené Mola, Jordi |
| author_facet |
Gené Mola, Jordi Ferrer Ferrer, Mar Hemming, Jochen van Dalfsen, Pieter de Hoog, Dirk Sanz Cortiella, Ricardo Rosell Polo, Joan R. Morros Rubió, Josep Ramon|||0000-0002-1395-487X Vilaplana Besler, Verónica|||0000-0001-6924-9961 Ruiz Hidalgo, Javier|||0000-0001-6774-685X Gregorio López, Eduard |
| author_role |
author |
| author2 |
Ferrer Ferrer, Mar Hemming, Jochen van Dalfsen, Pieter de Hoog, Dirk Sanz Cortiella, Ricardo Rosell Polo, Joan R. Morros Rubió, Josep Ramon|||0000-0002-1395-487X Vilaplana Besler, Verónica|||0000-0001-6924-9961 Ruiz Hidalgo, Javier|||0000-0001-6774-685X Gregorio López, Eduard |
| author2_role |
author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Computer vision Agriculture -- Automation Apples Instance segmentation Modal segmentation Amodal segmentation Yield prediction Depth image Fruit measurement Fruit visibility Agricultural robotics Dataset Visió per ordinador Agricultura -- Automatització Pomes Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Robòtica |
| topic |
Computer vision Agriculture -- Automation Apples Instance segmentation Modal segmentation Amodal segmentation Yield prediction Depth image Fruit measurement Fruit visibility Agricultural robotics Dataset Visió per ordinador Agricultura -- Automatització Pomes Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::Informàtica::Robòtica |
| description |
Refers to: Gené-Mola, J. [et al.]. Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation. "Computers and electronics in agriculture", Juny 2023, vol. 209, article 107854. https://doi.org/10.1016/j.compag.2023.107854 http://hdl.handle.net/2117/387035 |
| publishDate |
2024 |
| dc.date.none.fl_str_mv |
2024 2024-02-01 2024 2024-01-25 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2117/400254 https://dx.doi.org/10.1016/j.dib.2023.110000 |
| url |
https://hdl.handle.net/2117/400254 https://dx.doi.org/10.1016/j.dib.2023.110000 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.relation.none.fl_str_mv |
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-117142GB-I00 APRENDIZAJE PROFUNDO EFICIENTE PARA SECUENCIAS DE VIDEO Y NUBES DE PUNTOS |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
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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:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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Universitat Politècnica de Catalunya (UPC) |
| reponame_str |
UPCommons. Portal del coneixement obert de la UPC |
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UPCommons. Portal del coneixement obert de la UPC |
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| _version_ |
1869413858338144256 |
| spelling |
AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimationGené Mola, JordiFerrer Ferrer, MarHemming, Jochenvan Dalfsen, Pieterde Hoog, DirkSanz Cortiella, RicardoRosell Polo, Joan R.Morros Rubió, Josep Ramon|||0000-0002-1395-487XVilaplana Besler, Verónica|||0000-0001-6924-9961Ruiz Hidalgo, Javier|||0000-0001-6774-685XGregorio López, EduardComputer visionAgriculture -- AutomationApplesInstance segmentationModal segmentationAmodal segmentationYield predictionDepth imageFruit measurementFruit visibilityAgricultural roboticsDatasetVisió per ordinadorAgricultura -- AutomatitzacióPomesÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeoÀrees temàtiques de la UPC::Informàtica::RobòticaRefers to: Gené-Mola, J. [et al.]. Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation. "Computers and electronics in agriculture", Juny 2023, vol. 209, article 107854. https://doi.org/10.1016/j.compag.2023.107854 http://hdl.handle.net/2117/387035The present dataset comprises a collection of RGB-D apple tree images that can be used to train and test computer vision-based fruit detection and sizing methods. This dataset encompasses two distinct sets of data obtained from a Fuji and an Elstar apple orchards. The Fuji apple orchard sub-set consists of 3925 RGB-D images containing a total of 15,335 apples annotated with both modal and amodal apple segmentation masks. Modal masks denote the visible portions of the apples, whereas amodal masks encompass both visible and occluded apple regions. Notably, this dataset is the first public resource to incorporate on-tree fruit amodal masks. This pioneering inclusion addresses a critical gap in existing datasets, enabling the development of robust automatic fruit sizing methods and accurate fruit visibility estimation, particularly in the presence of partial occlusions. Besides the fruit segmentation masks, the dataset also includes the fruit size (calliper) ground truth for each annotated apple. The second sub-set comprises 2731 RGB-D images capturing five Elstar apple trees at four distinct growth stages. This sub-set includes mean diameter information for each tree at every growth stage and serves as a valuable resource for evaluating fruit sizing methods trained with the first sub-set. The present data was employed in the research paper titled “Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation” [1].This work was partly funded by the Departament de Recerca i Universitats de la Generali- tat de Catalunya (grant 2021 LLAV 0 0 088 ), the Spanish Ministry of Science, Innovation and Uni- versities (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142GB-I00 [DeeLight project] by MCIN/AEI/10.13039/50110 0 011033 and by “ERDF, a way of making Europe”, by the European Union). Data presented in this paper is also part of a Public Private Partnership project Precisie Tuinbouw, WP Fruit 4.0 (PPS KV 1604-025) and financed by Topsector Tuinbouw & Uitgangsmateriaal and various private companies. The work of Jordi GenéMola was supported by the Spanish Ministry of Universities through a Mar- garita Salas postdoctoral grant funded by the European Union - NextGenerationEU.Peer ReviewedElsevier20242024-02-0120242024-01-25journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/400254https://dx.doi.org/10.1016/j.dib.2023.110000reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2020-117142GB-I00 APRENDIZAJE PROFUNDO EFICIENTE PARA SECUENCIAS DE VIDEO Y NUBES DE PUNTOSopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4002542026-05-27T15:37:01Z |
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15.301603 |