Deep learning applied to equipment detection on flat roofs in images captured by UAV

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of Brasília-
Autor(es): dc.contributorUniversity of Brasília-
Autor(es): dc.contributorDom Bosco Catholic University-
Autor(es): dc.contributorDom Bosco Catholic University-
Autor(es): dc.contributorFederal University of Mato Grosso do Sul-
Autor(es): dc.creatorSantos, Lara Monalisa Alves dos-
Autor(es): dc.creatorZanoni, Vanda Alice Garcia-
Autor(es): dc.creatorBedin, Eduardo-
Autor(es): dc.creatorPistori, Hemerson-
Data de aceite: dc.date.accessioned2025-09-01T11:57:01Z-
Data de disponibilização: dc.date.available2025-09-01T11:57:01Z-
Data de envio: dc.date.issued2025-03-18-
Data de envio: dc.date.issued2025-03-18-
Data de envio: dc.date.issued2022-
Fonte completa do material: dc.identifierhttp://repositorio.unb.br/handle/10482/51959-
Fonte completa do material: dc.identifierhttps://doi.org/10.1016/j.cscm.2023.e01917-
Fonte completa do material: dc.identifierhttps://orcid.org/0000-0001-8181-760X-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1118976-
Descrição: dc.descriptionMaintenance on flat roofs is a complex activity. Equipment improperly positioned on flat roofs hinders the correct drainage of water and makes maintenance services more difficult. This article presents an experiment with deep learning algorithms involving 330 images acquired in 9 buildings by Unmanned Aerial Vehicle-UAV. This dataset was created by the authors to optimize decision-making for maintenance through automated processes and is being used for the first time in this article. The dataset refers to condenser equipment positioned on flat roofs and was tested in six state-of-the-art object-detection deep learning algorithms: Region-based convolutional neural networks (Faster R-CNN), Focal Loss (Retina-Net), Adaptive Training Sample Selection (ATSS), VarifocalNet (Vfnet), Side-Aware Boundary Localization (SABL) and FoveaBox (Fovea). Nine performance metrics were applied, achieving successful results by Faster R-CNN (Recall=0.93, Fscore=0.93, MAE=0.43) followed by ATSS (Precision=0.95). In a system with many variables, the target is the identification of the best algorithm capable of solving the proposed problem. In conclusion, the types of errors analyzed in detection alert to the diversity of causes related to the inherent characteristics of flat roofs that induce network confusion.-
Descrição: dc.descriptionFaculdade de Arquitetura e Urbanismo (FAU)-
Descrição: dc.descriptionDepartamento de Tecnologia em Arquitetura e Urbanismo (FAU TEC)-
Descrição: dc.descriptionPrograma de Pós-Graduação em Arquitetura e Urbanismo-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier Ltd.-
Direitos: dc.rightsAcesso Aberto-
Direitos: dc.rightsThis is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).-
Palavras-chave: dc.subjectTelhado plano - manutenção-
Palavras-chave: dc.subjectDetecção de equipamentos-
Palavras-chave: dc.subjectInspeção predial-
Palavras-chave: dc.subjectVeículos aéreos não tripulados (VANTs)-
Palavras-chave: dc.subjectVisão computacional-
Palavras-chave: dc.subjectAprendizado profundo-
Título: dc.titleDeep learning applied to equipment detection on flat roofs in images captured by UAV-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional – UNB

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