Automated classification of thermal defects in the building envelope using thermal and visible images-Reference-Cited by-同舟云学术

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Automated classification of thermal defects in the building envelope using thermal and visible images

Published:2022-01-31 Issue: Volume: Page:1-17
ISSN:1768-6733
Container-title:Quantitative InfraRed Thermography Journal
language:en
Short-container-title:Quantitative InfraRed Thermography Journal

Author:

Kim Changmin¹^ORCID,Park Gwanyong²,Jang Hyangin³,Kim Eui-Jong¹^ORCID

Affiliation:

1. Department of Architectural Engineering, Inha University, Incheon, South Korea

2. Mirae Environment Plan, Institute of Green Building and New Technology, Seoul, South Korea

3. Intelligence Research Institute, EG Architectural Solution, Seoul, South Korea

Funder

Korea Agency for Infrastructure Technology Advancement

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Instrumentation

Link

https://www.tandfonline.com/doi/pdf/10.1080/17686733.2022.2033531

Reference34 articles.

1. Thermographic methodologies used in infrastructure inspection: A review—Post-processing procedures

2. A review of infrared thermography for the investigation of building envelopes: Advances and prospects

3. Applications of the infrared thermography in the energy audit of buildings: A review

4. Infrared thermography for building diagnostics

5. Automatic thermographic and RGB texture of as-built BIM for energy rehabilitation purposes

Cited by 24 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Bayesian inversion for in-situ thermal characterisation of walls in the presence of thermal anomalies;Energy and Buildings;2024-09

2. Possibilities of Detecting and Evaluating Thermal Defects in Agricultural Building Envelopes Using Simultaneous Infrared Thermography and Computer Modelling;Acta Technologica Agriculturae;2024-09-01

3. Automatic segmentation of microporous defects in composite film materials based on the improved attention U-Net module;Quantitative InfraRed Thermography Journal;2024-08-22

4. A novel application of deep learning approach over IRT images for the automated detection of rising damp on historical masonries;Case Studies in Construction Materials;2024-07

5. Energy-efficient buildings with energy-efficient optimized models: a case study on thermal bridge detection;Cluster Computing;2024-06-17

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