Dataset of manually classified images obtained from a construction site

Descripción del Articulo

A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset consists of 1046 images selected from video footage by a frame extraction algorit...

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Detalles Bibliográficos
Autores: Del Savio, Alexandre Almeida, Luna Torres, Ana Felícita, Cárdenas Salas, Daniel Enrique, Vergara Olivera, Mónica Alejandra, Urday Ibarra, Gianella Tania
Formato: artículo
Fecha de Publicación:2022
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/17770
Enlace del recurso:https://hdl.handle.net/20.500.12724/17770
https://doi.org/10.1016/j.dib.2022.108042
Nivel de acceso:acceso abierto
Materia:Building
Neural networks (Computer science)
Pattern perception
Image processing
Construcción
Redes neuronales (Informática)
Reconocimiento de formas
Proceso de imágenes
https://purl.org/pe-repo/ocde/ford#2.01.03
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dc.title.en_EN.fl_str_mv Dataset of manually classified images obtained from a construction site
title Dataset of manually classified images obtained from a construction site
spellingShingle Dataset of manually classified images obtained from a construction site
Del Savio, Alexandre Almeida
Building
Neural networks (Computer science)
Pattern perception
Image processing
Construcción
Redes neuronales (Informática)
Reconocimiento de formas
Proceso de imágenes
https://purl.org/pe-repo/ocde/ford#2.01.03
title_short Dataset of manually classified images obtained from a construction site
title_full Dataset of manually classified images obtained from a construction site
title_fullStr Dataset of manually classified images obtained from a construction site
title_full_unstemmed Dataset of manually classified images obtained from a construction site
title_sort Dataset of manually classified images obtained from a construction site
author Del Savio, Alexandre Almeida
author_facet Del Savio, Alexandre Almeida
Luna Torres, Ana Felícita
Cárdenas Salas, Daniel Enrique
Vergara Olivera, Mónica Alejandra
Urday Ibarra, Gianella Tania
author_role author
author2 Luna Torres, Ana Felícita
Cárdenas Salas, Daniel Enrique
Vergara Olivera, Mónica Alejandra
Urday Ibarra, Gianella Tania
author2_role author
author
author
author
dc.contributor.other.none.fl_str_mv Del Savio, Alexandre Almeida
Luna Torres, Ana Felícita
Cárdenas Salas, Daniel Enrique
Vergara Olivera, Mónica Alejandra
dc.contributor.student.none.fl_str_mv Urday Ibarra, Gianella Tania (Ingeniería de Sistemas)
dc.contributor.author.fl_str_mv Del Savio, Alexandre Almeida
Luna Torres, Ana Felícita
Cárdenas Salas, Daniel Enrique
Vergara Olivera, Mónica Alejandra
Urday Ibarra, Gianella Tania
dc.subject.en_EN.fl_str_mv Building
Neural networks (Computer science)
Pattern perception
Image processing
topic Building
Neural networks (Computer science)
Pattern perception
Image processing
Construcción
Redes neuronales (Informática)
Reconocimiento de formas
Proceso de imágenes
https://purl.org/pe-repo/ocde/ford#2.01.03
dc.subject.es_PE.fl_str_mv Construcción
Redes neuronales (Informática)
Reconocimiento de formas
Proceso de imágenes
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.01.03
description A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset consists of 1046 images selected from video footage by a frame extraction algorithm and txt files containing the objects' class and coordinates information. These data can be used to develop computer vision techniques in the engineering and construction fields.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2023-03-01T16:00:51Z
dc.date.available.none.fl_str_mv 2023-03-01T16:00:51Z
dc.date.issued.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.other.none.fl_str_mv Artículo en Scopus
format article
dc.identifier.citation.es_PE.fl_str_mv Del Savio A., Luna A., Cárdenas-Salas D., Vergara M. & Urday, G. (2022). Dataset of manually classified images obtained from a construction site. Data in Brief, 42. http://doi.org/10.1016/j.dib.2022.108042
dc.identifier.issn.none.fl_str_mv 2352-3409
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12724/17770
dc.identifier.journal.none.fl_str_mv Data in Brief
dc.identifier.isni.none.fl_str_mv 0000000121541816
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.dib.2022.108042
dc.identifier.scopusid.none.fl_str_mv 2-s2.0-85126605198
identifier_str_mv Del Savio A., Luna A., Cárdenas-Salas D., Vergara M. & Urday, G. (2022). Dataset of manually classified images obtained from a construction site. Data in Brief, 42. http://doi.org/10.1016/j.dib.2022.108042
2352-3409
Data in Brief
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url https://hdl.handle.net/20.500.12724/17770
https://doi.org/10.1016/j.dib.2022.108042
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv urn:issn: 2352-3409
dc.rights.*.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
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dc.format.none.fl_str_mv application/html
dc.publisher.none.fl_str_mv Elsevier
dc.publisher.country.none.fl_str_mv NL
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Repositorio Institucional - Ulima
Universidad de Lima
reponame:ULIMA-Institucional
instname:Universidad de Lima
instacron:ULIMA
instname_str Universidad de Lima
instacron_str ULIMA
institution ULIMA
reponame_str ULIMA-Institucional
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spelling Del Savio, Alexandre AlmeidaLuna Torres, Ana FelícitaCárdenas Salas, Daniel EnriqueVergara Olivera, Mónica AlejandraUrday Ibarra, Gianella TaniaDel Savio, Alexandre AlmeidaLuna Torres, Ana FelícitaCárdenas Salas, Daniel EnriqueVergara Olivera, Mónica AlejandraUrday Ibarra, Gianella Tania (Ingeniería de Sistemas)2023-03-01T16:00:51Z2023-03-01T16:00:51Z2022Del Savio A., Luna A., Cárdenas-Salas D., Vergara M. & Urday, G. (2022). Dataset of manually classified images obtained from a construction site. Data in Brief, 42. http://doi.org/10.1016/j.dib.2022.1080422352-3409https://hdl.handle.net/20.500.12724/17770Data in Brief0000000121541816https://doi.org/10.1016/j.dib.2022.1080422-s2.0-85126605198A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset consists of 1046 images selected from video footage by a frame extraction algorithm and txt files containing the objects' class and coordinates information. These data can be used to develop computer vision techniques in the engineering and construction fields.application/htmlengElsevierNLurn:issn: 2352-3409info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/Repositorio Institucional - UlimaUniversidad de Limareponame:ULIMA-Institucionalinstname:Universidad de Limainstacron:ULIMABuildingNeural networks (Computer science)Pattern perceptionImage processingConstrucciónRedes neuronales (Informática)Reconocimiento de formasProceso de imágeneshttps://purl.org/pe-repo/ocde/ford#2.01.03Dataset of manually classified images obtained from a construction siteinfo:eu-repo/semantics/articleArtículo en ScopusDel Savio, Alexandre Almeida (No figura en la lista del año 2021-2)Luna Torres, Ana Felícita (Ingeniería Civil)Cárdenas Salas, Daniel Enrique (Ingeniería de Sistemas)Vergara Olivera, Mónica Alejandra (Ingeniería Civil)Del Savio, Alexandre Almeida (Faculty of Engineering and Architecture, Universidad de Lima)Luna Torres, Ana Felícita (Faculty of Engineering and Architecture, Universidad de Lima)Cárdenas Salas, Daniel Enrique (Faculty of Engineering and Architecture, Universidad de Lima)Vergara Olivera, Mónica Alejandra (Faculty of Engineering and Architecture, Universidad de Lima)OILICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ulima.edu.pe/bitstream/20.500.12724/17770/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5220.500.12724/17770oai:repositorio.ulima.edu.pe:20.500.12724/177702024-11-08 16:16:02.905Repositorio Universidad de Limarepositorio@ulima.edu.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Dataset of manually classified images obtained from a construction site
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