IRT (Infrared Thermography) is a commonly used non-destructive testing method for detecting thermal anomalies of a building envelope that may cause heat loss and occupant discomfort. Despite its importance, a thermal anomaly is still usually detected by manual analysis of IRT, which strongly depends on the analyzer's experience. In this study, an automatic anomaly detection framework from thermal and visible images was developed. The wall, which is the subject of anomaly detection, is segmented from the visible image by a CNN (Convolutional Neural Network). The temperature threshold of the anomaly area is determined from the multimodal temperature distribution of the target domain. The performance of the anomaly detection was improved by applying the segmentation process (F₁ score 0.497 to 0.808). The framework proposed in this study is expected to be implemented through portable devices and enable instant in-situ thermal anomaly detection.

IRT（红外热成像）是一种常用的非破坏性测试方法，用于检测建筑围护结构的热异常，该异常可能导致热量损失和乘员不适。尽管异常重要，但通常仍需通过对IRT进行手动分析来检测热异常，这在很大程度上取决于分析仪的经验。在这项研究中，从热图像和可见图像开发了一种自动异常检测框架。通过CNN（卷积神经网络）从可见图像中分割出作为异常检测对象的墙。异常区域的温度阈值由目标域的多峰温度分布确定。通过应用分割过程可以改善异常检测的性能（F₁得分0.497至0.808）。