A Data Fusion Platform for Supporting Bridge Deck Condition Monitoring by Merging Aerial and Ground Inspection Imagery
This work addresses the need for improved bridge deck monitoring by enabling more efficient inspection procedures, though it appears incremental as it builds on existing two-phase inspection methods.
The study tackled the problem of fusing multi-scale aerial and ground inspection images for bridge deck condition monitoring by introducing a data fusion platform that uses geo-referencing and a web-based interface, with a case study demonstrating its implementation using optical and infrared images from UAVs and ground inspections.
UAVs showed great efficiency on scanning bridge decks surface by taking a single shot or through stitching a couple of overlaid still images. If potential surface deficits are identified through aerial images, subsequent ground inspections can be scheduled. This two-phase inspection procedure showed great potentials on increasing field inspection productivity. Since aerial and ground inspection images are taken at different scales, a tool to properly fuse these multi-scale images is needed for improving the current bridge deck condition monitoring practice. In response to this need a data fusion platform is introduced in this study. Using this proposed platform multi-scale images taken by different inspection devices can be fused through geo-referencing. As part of the platform, a web-based user interface is developed to organize and visualize those images with inspection notes under users queries. For illustration purpose, a case study involving multi-scale optical and infrared images from UAV and ground inspector, and its implementation using the proposed platform is presented.