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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.02963 |
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| _version_ | 1866911771043823616 |
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| author | Kurtser, Polina Feng, Kailun Olofsson, Thomas De Andres, Aitor |
| author_facet | Kurtser, Polina Feng, Kailun Olofsson, Thomas De Andres, Aitor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02963 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | One-class anomaly detection through color-to-thermal AI for building envelope inspection Kurtser, Polina Feng, Kailun Olofsson, Thomas De Andres, Aitor Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | One-class anomaly detection through color-to-thermal AI for building envelope inspection |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.02963 |