| Campo DC | Valor | Idioma |
|---|
| dc.contributor.author | Santos, Lara Monalisa Alves dos | - |
| dc.contributor.author | Lescano, Leonardo Rabero | - |
| dc.contributor.author | Higa, Gabriel Toshio Hirokawa | - |
| dc.contributor.author | Zanoni, Vanda Alice Garcia | - |
| dc.contributor.author | Silva, Lenildo Santos da | - |
| dc.contributor.author | Alvarez Mendoza, Cesar Ivan | - |
| dc.contributor.author | Pistori, Hemerson | - |
| dc.date.accessioned | 2025-03-17T12:33:10Z | - |
| dc.date.available | 2025-03-17T12:33:10Z | - |
| dc.date.issued | 2024-12-18 | - |
| dc.identifier.citation | SANTOS, Lara Monalisa Alves dos et al. Mapping stains on flat roofs using semantic segmentation based on deep learning. Case Studies in Construction Materials, [S. l.], v. 22, e04106, 2024. DOI: https://doi.org/10.1016/j.cscm.2024.e04106. Disponível em: https://www.sciencedirect.com/science/article/pii/S2214509524012580?via%3Dihub. Acesso em: 11 mar. 2025. | pt_BR |
| dc.identifier.uri | http://repositorio.unb.br/handle/10482/51900 | - |
| dc.language.iso | eng | pt_BR |
| dc.publisher | Elsevier Ltd. | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | Mapping stains on flat roofs using semantic segmentation based on deep learning | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Aprendizagem profunda | pt_BR |
| dc.subject.keyword | Inspeção predial | pt_BR |
| dc.subject.keyword | Drones | pt_BR |
| dc.subject.keyword | Visão computacional | pt_BR |
| dc.rights.license | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | pt_BR |
| dc.identifier.doi | https://doi.org/10.1016/j.cscm.2024.e04106 | pt_BR |
| dc.description.abstract1 | Moisture stains indicate ongoing degradation processes and may reveal areas of the roof slab
where water infiltration occurs, compromising the performance and durability of the building
system. During inspections of roofing systems, an inspector’s field of vision differs from that of
drones during overflights. As a result, traditional inspections might not always detect the presence
and severity of stains, making maintenance on flat roofs a complex task. In this context, this
experimental study aims to analyze deep learning-based semantic segmentation with images
obtained from drones to map and monitor damp patches during automated building inspections
of flat roof systems. The research tested two convolutional neural networks for semantic segmentation: the Fully Convolutional Network (FCN) with a ResNet50 backbone and DeepLabV3
with a ResNet101 backbone, as well as a transformer-based deep artificial neural network called
SegFormer with a MiT-B1 backbone. We evaluated three optimizers for each model—Adam,
Adagrad, and SGD—along with learning rates of 1e-2, 1e-3, and 1e-4. The models were compared
using four performance metrics. The FCN, optimized with Adagrad at a learning rate of 1e-2,
showed the best results. The average metrics obtained in this case were as follows: precision:
79.69 %, recall: 67.81 %, F-score: 73.09 %, and Intersection over Union (IoU): 57.70 %. | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0002-8022-2513 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0009-0004-3125-9696 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0009-0006-6771-0076 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0003-2629-4214 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0001-5099-6123 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0001-5629-0893 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0001-8181-760X | pt_BR |
| dc.contributor.affiliation | University of Brasilia | pt_BR |
| dc.contributor.affiliation | Dom Bosco Catholic University | pt_BR |
| dc.contributor.affiliation | Dom Bosco Catholic University | pt_BR |
| dc.contributor.affiliation | University of Brasilia | pt_BR |
| dc.contributor.affiliation | University of Brasilia | pt_BR |
| dc.contributor.affiliation | University of Augsburg, Centre for Climate Resilience | pt_BR |
| dc.contributor.affiliation | Salesian Polytechnic University, Environmental Research Group for Sustainable Development (GIADES) | pt_BR |
| dc.contributor.affiliation | Dom Bosco Catholic University | pt_BR |
| dc.contributor.affiliation | Federal University of Mato Grosso do Sul | pt_BR |
| dc.description.unidade | Faculdade de Arquitetura e Urbanismo (FAU) | pt_BR |
| dc.description.unidade | Departamento de Tecnologia em Arquitetura e Urbanismo (FAU TEC) | pt_BR |
| dc.description.unidade | Faculdade de Tecnologia (FT) | pt_BR |
| dc.description.unidade | Departamento de Engenharia Civil e Ambiental (FT ENC) | pt_BR |
| dc.description.ppg | Programa de Pós-Graduação em Arquitetura e Urbanismo | pt_BR |
| Aparece nas coleções: | Artigos publicados em periódicos e afins
|
