One-class anomaly detection through color-to-thermal AI for building envelope inspection
This is an incremental method for building inspection professionals to automate anomaly detection without labels.
The paper tackles the problem of detecting anomalies in building envelope inspections by predicting thermal distributions from color images using AI, and it demonstrated the method's ability to detect thermal bridges by training on data at different outdoor temperatures.
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.