Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/app11030931
Keywords
thermal bridge; infrared thermal image; image processing; neural network; building energy
Categories
Funding
- National Research Foundation of Korea (NRF) - Korean government (MSIT) [NRF-2018R1D1A1B07041890]
- Korea Agency for Infrastructure Technology Advancement (KAIA) - Ministry of Land, Infrastructure and Transport [20CTAP-C152248-02]
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A linear thermal bridge detection method based on image processing and machine learning was proposed in this study, and its effectiveness was validated by detecting thermal bridges in actual buildings, achieving high levels of precision, recall, and F-score.
Detecting thermal bridges in building envelopes should be a priority to improve the thermal performance of buildings. Recently, thermographic surveys are being used to detect thermal bridges. However, conventional methods of detecting thermal bridges from thermal images rely on the subjective judgment of audits. Research has been conducted to automatically detect thermal bridges from thermal images to improve problems caused by such subjective judgment, but most of these studies are still in the early stage. Therefore, this study proposes a linear thermal bridge detection method based on image processing and machine learning. The proposed method includes thermal anomaly area clustering, feature extraction, and an artificial-neural-network-based thermal bridge detection. The proposed method was validated by detecting the thermal bridges in actual buildings. As a result, the average precision, recall, and F-score were 89.29%, 87.29, and 87.63%, respectively.
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