Abstract
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.
Highlights
According to ISO 10211:2007, a thermal bridge is defined as “part of the building envelope where the otherwise uniform thermal resistance is significantly changed by full or partial penetration of the building envelope” [1]
This study proposes a method for detecting linear thermal bridges from thermal images based on machine learning
This study proposed a new method for detecting linear thermal bridges from thermal images, using image processing and artificial neural networks (ANNs)
Summary
According to ISO 10211:2007, a thermal bridge is defined as “part of the building envelope where the otherwise uniform thermal resistance is significantly changed by full or partial penetration of the building envelope” [1]. Thermographic surveys are recently being used as a useful method to detect thermal bridges in building envelopes [8]. Thermal anomalies, including thermal bridges in buildings, affect the surface temperature of the envelope. The auditor moves around the building and uses a thermal imaging camera to check the temperature distribution of the building envelope. If the auditor finds areas with abnormal temperature distribution in the thermal images, the auditor analyzes these areas During this analysis, the auditor generally uses experience and subjective judgment to detect thermal bridges [12,13]. The auditor generally uses experience and subjective judgment to detect thermal bridges [12,13] As these results are significantly influenced by
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