Low-cost training data creation for crack detection using an attention mechanism in deep learning models

Abstract

The aging of concrete structures such as bridges and tunnels has led to the manifestation of damage, posing a significant problem. Particularly, the detection, evaluation, and documentation of cracks, which are a crucial indicator affecting the rate of deterioration, require an immense amount of time and effort. Consequently, the development of automatic detection methods using machine learning techniques has been pursued. However, the automatic pixel-level detection of cracks from captured images necessitates a large quantity of teacher images labeled at the same pixel level, which are costly to produce. Creating these images is not straightforward and has been a barrier to the practical implementation of image analysis methods. In response, this study developed a technique for detecting cracks at the pixel level while reducing the cost of creating teacher data, utilizing the attention mechanism. Additionally, the accuracy of this method was evaluated using captured images, confirming its equivalence to existing detection methods in terms of precision. This paper is the English translation of the authors’ previous work [Izumi and Chun, (2021). "Crack detection using deep learning with attention mechanisms" Artificial Intelligence and Data Science, 2(J2), 545-555. (in Japanese)].