We present a label-free method for detecting anomalies during thermographic inspection of building envelopes. It is based on the AI-driven prediction of thermal distributions from color images. Effectively the method performs as a one-class classifier of the thermal image regions with high mismatch between the predicted and actual thermal distributions. The algorithm can learn to identify certain features as normal or anomalous by selecting the target sample used for training. We demonstrated this principle by training the algorithm with data collected at different outdoors temperature, which lead to the detection of thermal bridges. The method can be implemented to assist human professionals during routine building inspections or combined with mobile platforms for automating examination of large areas.

翻译：我们提出了一种无标签方法，用于在建筑围护结构热成像检测中识别异常。该方法基于AI驱动的彩色图像热分布预测，通过筛选预测热分布与实际热分布差异显著的热图像区域，实现单类分类器的功能。算法可通过选择训练目标样本，学习将特定特征判为正常或异常。我们通过在不同室外温度下采集的数据训练算法，验证了这一原理并成功检测到热桥。该方法可辅助专业人员进行常规建筑检测，或与移动平台结合实现大面积区域的自动化检测。