Automatic detection of concrete floating and delamination by analyzing thermal images through self-supervised learning

Abstract

Infrared methods that can remotely detect internal damage by capturing thermal images often miss damaged areas when the judgment is made by humans. Additionally, although there have been moves to introduce autodiscovery through convolutional neural networks as part of infrared technology, such methods have not had a sufficient level of precision due to a lack of supervised training data. Hence, in this study, we focus on self-supervised learning. In self-supervised learning, even if there is a lack of supervised labels, it is still possible to realize a high degree of accuracy. Moreover, we present an example of introducing self-supervised learning via the infrared method and validate the effectiveness of the same. This paper is the English translation from the authors’ previous work [Kawanowa, S. et. al., (2022). "Automatic detection of inner defects of concrete by analyz-ing thermal images using self-supervised learning." Artificial Intelligence and Data Science, 3(J2), 47-55. (in Japanese)].