Vision transformer-enhanced thermal anomaly detection in building facades through fusion of thermal and visible imagery

Abstract

The thermal anomaly detection of building facades is critically important for the evaluation and upkeep of structures. While conventional methods are effective in numerous instances, their capability to discern flaws in complex environments is constrained. This research employs an optimized Vision Transformer (ViT) technology, enhanced by the Line Acquisition, Filtering, and Revision (LAFR) algorithm. By amalgamating the distinctive advantages of ViT’s attention-based mechanisms with the complementary data from thermal and visible imagery, this approach significantly bolsters the detection of thermal anomalies on building facades. The experimental outcomes reveal that our methodology, with an F1 score of 0.847, markedly surpasses traditional thermal imaging (F1 score of 0.466), showcasing its potential for precise anomaly detection in architectural contexts. This validates the method as a more robust and efficient solution for structural evaluation, promising to elevate the quality of architectural inspections and maintenance.

Keywords

• thermal anomaly detection
• facade inspection
• image segmentation
• vision transformer
• thermal imaging