Detection of Bridge Damages by Image Processing Using the Deep Learning Transformer Model

Abstract

In Japan, bridges are inspected via close visual examinations every five years. However, these inspections are labor intensive, and a shortage of engineers and budget constraints will restrict such inspections in the future. In recent years, efforts have been made to reduce the labor required for inspections by automating various aspects of the inspection process. In particular, image processing technology, such as transformer models, has been used to automatically detect damage in images of bridges. However, there has been insufficient discussion on the practicality of applying such models to damage detection. Therefore, this study demonstrates how they may be used to detect bridge damage. In particular, delamination and rebar exposure are targeted using three different models trained with datasets containing different size images. The detection results are compared and evaluated, which shows that the detection performance of the transformer model can be improved by increasing the size of the input image. Moreover, depending on the target, it may be desirable to avoid changing the detection target. The result of the largest size of the input image shows that around 3.9% precision value or around 19.9% recall value is higher than one or the other models.