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dc.contributor.authorDel Savio, Alexandre Almeida
dc.contributor.authorLuna Torres, Ana Felícita
dc.contributor.authorCárdenas Salas, Daniel Enrique
dc.contributor.authorVergara Olivera, Mónica Alejandra
dc.contributor.authorUrday Ibarra, Gianella Tania
dc.contributor.otherDel Savio, Alexandre Almeida
dc.contributor.otherLuna Torres, Ana Felícita
dc.contributor.otherVergara Olivera, Mónica Alejandra
dc.date.accessioned2025-09-09T21:26:35Z
dc.date.available2025-09-09T21:26:35Z
dc.date.issued2025
dc.identifier.issn2352-3409
dc.identifier.urihttps://hdl.handle.net/20.500.12724/23193
dc.description.abstractThis data paper presents a manually labeled dataset of 1,214 images of personnel captured from a construction site using four static cameras. There are two classes, standing and people leaning. The classification is stored in accompanying text files and bounding box coordinates for every image. The compilation was done to support the developing and validation computer vision and AI models for construction site monitoring. This dataset addresses the challenges of finding personnel in different poses within complex construction environments. The resource will enhance construction site safety monitoring and personnel activity analysis by allowing more precise neural network training. The dataset is stored in a public repository, making it openly accessible for academic and industrial purposes regarding computer vision, civil engineering, and workplace safety.
dc.formatapplication/html
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofurn:issn: 2352-3409
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectPendiente
dc.titleManually classified dataset of leaning and standing personnel images for construction site monitoring and neural network trainingen_EN
dc.typeinfo:eu-repo/semantics/article
dc.identifier.journalData Brief
dc.publisher.countryGB
dc.type.otherArtículo (Scopus)
dc.identifier.isni121541816
dc.contributor.studentUrday Ibarra, Gianella Tania (Ingeniería de Sistemas)
dc.subject.ocdePendiente
dc.identifier.doihttps://doi.org/10.1016/j.dib.2025.111516
dc.identifier.scopusid2-s2.0-105001431339


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