Acquisition of feature representation of record data via graph neural network to support determination of deterioration levels

Abstract

In this paper, we propose a method of acquiring feature representations of record data via the graph neural network to assist in determining the deterioration levels. In the inspection work, multiple deformation images are captured from different angles and distances and stored as record data. However, conventional studies on the deterioration level classification assume the input of a single image for model learning. This makes it difficult to handle the input of record data that has different properties from those of a single image. Therefore, in this paper, to deal with record data, which is a group of multiple images, we construct a graph neural network that can learn the relationship between the individual images and the record data. Therefore, we can acquire feature representations of the record data. In the last part of the paper, the effectiveness of the proposed method is verified through experiments using deformation images obtained during actual inspections.