# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 1 Introduction  
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]. Thermal bridges account for a significant proportion of the heating load in buildings [2]. Previous studies have reported that thermal bridges increase the heating load by up to 30% [3, 4]. Thermal bridges not only increase the amount of energy consumed to cool and heat buildings, but also lower the thermal comfort of occupants because of cold outer walls [5] and cause serious problems, such as condensation [6]. As described above, thermal bridges in building envelopes have negative effects on the thermal performance of buildings, so detecting the location of these thermal bridges helps to develop a strategy to retrofit building envelopes [7]. Therefore, detecting thermal bridges in building envelopes should be a priority to improve the thermal performance of buildings. 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. An infrared thermal imaging camera is a device that converts the radiation emitted by an object into temperature and presents it in the form of images [9]. The infrared thermal images acquired by a thermal imaging camera shows thermal anomalies caused by the surface temperature differences in the envelope, and an audit can visually detect thermal defects in building envelopes through these images [10, 11]. In general, a thermographic survey proceeds as follows. First, 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]. Therefore, as these results are significantly influenced bythe auditor's experience and subjective judgment, the auditor may either identify or fail to detect actual thermal bridges.  
Recently, several studies have been performed to automatically detect thermal bridges from thermal images, to improve problems caused by reliance on auditors [8; 12]. Previous studies have shown that thermal bridges can be detected from thermal images, based on thresholds such as the temperature difference and the shape of thermal bridges. However, threshold-based methods produce good results in certain cases, but there are difficulties in finding an optimized threshold applicable to all cases [14]. In the case of thermal images of building envelopes, the objects displaying abnormal temperature are different depending on the target and time of imaging. Furthermore, the thermal bridges in the thermal images always change in temperature and shape, depending on the circumstances. In such complicated situations, machine learning, rather than thresholds, can increase the detection accuracy in various cases [15; 16].  
This study proposes a method for detecting linear thermal bridges from thermal images based on machine learning. This article is structured as follows. Section 2 reviews studies on detecting thermal anomalies from thermal images. Section 3 describes the machine-learning-based thermal bridge detection method proposed in this study. Section 4 presents the results of applying this method to actual thermal images. Finally, Section 5 describes the conclusions of this study and future research.