One-class anomaly detection through color-to-thermal AI for building
  envelope inspection
Polina Kurtser Kailun Feng Thomas Olofsson Aitor De Andres
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.