One-class anomaly detection through color-to-thermal AI for building envelope inspection
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 51 references that are in the map. First posted on arXiv 5 Feb 2024.
Builds on
6 of 51 references.
References · 6
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 263
- 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
- 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
- 2013One-Class Classification: Taxonomy of Study and Review of Techniquescited by 68
- 2023Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Surveycited by 10
Built on by
Not cited yet.