Recording of bridge damage areas by 3D integration of multiple images and reduction of the variability in detected results

Machine learning models have been developed to perform damage detection using images to improve bridge inspection efficiency. However, in damage detection using images alone, the 3D coordinates of the damage cannot be recorded. Furthermore, the accuracy of the detection depends on the quality of the images. This paper proposes a method to integrate and record the damage detected from multiple images into a 3D model using deep learning to detect the damage from bridge images and structure from motion to identify the shooting position. The proposed method reduces the variability of the detection results between images and can assess the scale of damage or, conversely, where there is no damage and the extent of inspection omissions. The proposed method has been applied to a real bridge, and it has been shown that the actual damage locations can be recorded as a 3D model.

Automated summary

This paper proposes a method that uses deep learning and structure from motion to integrate and record damage detected from multiple images into a 3D model. The method improves bridge inspection efficiency and allows for assessment of the damage scale and inspection omissions.