A Data Fusion Platform for Supporting Bridge Deck Condition Monitoring by Merging Aerial and Ground Inspection Imagery

Abstract

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.