AUTOMATIC DETECTION OF INNER DEFECTS OF CONCRETE BY ANALYZING THERMAL IMAGES USING SELF-SUPERVISED LEARNING

Abstract

In the infrared thermography method, which remotely detects inner defects by capturing thermal images of concrete, damaged areas are often overlooked by human judgment. Although there is a movement to introduce CNN-based automatic detection to the infrared method, sufficient accuracy has not been obtained due to the lack of training data. Therefore, in this study, we focus on self-supervised learning. Self-supervised learning has the potential to achieve high accuracy with fewer teacher labels. In this study, we present an example of how to introduce self-supervised learning to the infrared thermography method and verify its effectiveness.