Structure from segmented motion for bridge 3D damage detection using UAV, AI, and MR

Abstract

Periodical bridge inspection is essential to monitor the deterioration and maintenance progress. However, traditional inspection method requires a lot of works, expensive equipments, and time costly, and its difficult to implement periodically specially in developing countries. Therefore, this study proposed a Structure-from-Segmented-Motion (SfSM) method to enhance the bridge vision based inspection process. This method can localize and visualize the damage location in the bridge by utilizing various technologies such as UAV for data gathering, deep learning methods for damage segmentation, and Mixed Reality (MR) for digital transformation (DX). Firstly, optimal flight path for a single span I-girder bridge using UAV was proposed. This helps to fasten the visual bridge inspection, reduce workforce, and inspect some difficult to access parts without the use of expensive equipments. Then, the trained Deeplabv3+ was used to segment the corrosion damages of the images gathered. Finally, the 3D bridge model with and without the segmented corrosion were reconstructred using SfM and SfSM to visualize the location of damage in the whole bridge and viewed in a mixed reality (MR) platform. The proposed method will help the engineers to evaluate the bridge condition remotely which will save time and makes it safer.