A few-shot learning approach for the segmentation of subsurface defects in thermography images of concrete structures

Sandra Pozzer; Gabriel Ramos; Ehsan Rezazadeh Azar; Ahmad Osman; Ahmed El Refai; Fernando López; Clemente Ibarra-Castanedo; Xavier Maldague

doi:10.1117/12.3013684

7 June 2024 A few-shot learning approach for the segmentation of subsurface defects in thermography images of concrete structures

Sandra Pozzer, Gabriel Ramos, Ehsan Rezazadeh Azar, Ahmad Osman, Ahmed El Refai, Fernando López, Clemente Ibarra-Castanedo, Xavier Maldague

Author Affiliations +

Proceedings Volume 13047, Thermosense: Thermal Infrared Applications XLVI; 130470F (2024) https://doi.org/10.1117/12.3013684
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The identification and categorization of subsurface damages in thermal images of concrete structures remain an ongoing challenge that demands expert knowledge. Consequently, creating a substantial number of annotated samples for training deep neural networks poses a significant issue. Artificial intelligence (AI) models particularly encounter the problem of false positives arising from thermal patterns on concrete surfaces that do not correspond to subsurface damages. Such false detections would be easily identifiable in visible images, underscoring the advantage of possessing additional information about the sample surface through visible imaging. In light of these challenges, this study proposes an approach that employs a few-shot learning method known as the Siamese Neural Network (SNN), to frame the problem of subsurface delamination detection in concrete structures as a multi-modal similarity region comparison problem. The proposed procedure is evaluated using a dataset comprising 500 registered pairs of infrared and visible images captured in various infrastructure scenarios. Our findings indicate that leveraging prior knowledge regarding the similarity between visible and thermal data can significantly reduce the rate of false positive detection by AI models in thermal images.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Sandra Pozzer, Gabriel Ramos, Ehsan Rezazadeh Azar, Ahmad Osman, Ahmed El Refai, Fernando López, Clemente Ibarra-Castanedo, and Xavier Maldague "A few-shot learning approach for the segmentation of subsurface defects in thermography images of concrete structures", Proc. SPIE 13047, Thermosense: Thermal Infrared Applications XLVI, 130470F (7 June 2024); https://doi.org/10.1117/12.3013684

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