Título:	Deep learning applied to equipment detection on flat roofs in images captured by UAV
Autor(es):	Santos, Lara Monalisa Alves dos Zanoni, Vanda Alice Garcia Bedin, Eduardo Pistori, Hemerson
ORCID:	https://orcid.org/0000-0001-8181-760X
Afiliação do autor:	University of Brasília University of Brasília Dom Bosco Catholic University Dom Bosco Catholic University Federal University of Mato Grosso do Sul
Assunto:	Telhado plano - manutenção Detecção de equipamentos Inspeção predial Veículos aéreos não tripulados (VANTs) Visão computacional Aprendizado profundo
Data de publicação:	2023
Editora:	Elsevier Ltd.
Referência:	SANTOS, Lara Monalisa Alves dos et al. Deep learning applied to equipment detection on flat roofs in images captured by UAV. Case Studies in Construction Materials, [S. l.], v. 18, e01917, Jul. 2023. DOI: https://doi.org/10.1016/j.cscm.2023.e01917. Disponível em: https://www.sciencedirect.com/science/article/pii/S2214509523000967?via%3Dihub#sec0005. Acesso em: 18 mar. 2025.
Abstract:	Maintenance 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.
Unidade Acadêmica:	Faculdade de Arquitetura e Urbanismo (FAU) Departamento de Tecnologia em Arquitetura e Urbanismo (FAU TEC)
Programa de pós-graduação:	Programa de Pós-Graduação em Arquitetura e Urbanismo
Licença:	This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
DOI:	https://doi.org/10.1016/j.cscm.2023.e01917
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