Automated bridge component recognition using close-range images from unmanned aerial vehicles

Unmanned aerial vehicles (UAVs), in conjunction with computer vision techniques, have shown great potential for bridge inspections. Close-range images captured in proximity to the structural surface are generally required to detect damage and also need to be linked to the corresponding structural component to enable assessment of the health of the global structure. However, the lack of contextual information makes automated identification of bridge components in close-range images challenging. This study proposes a framework for automated bridge component recognition based on close-range images collected by UAVs. First, a 3D point cloud is generated from the UAV survey of the bridge and segmented into bridge components. The segmented point cloud is subsequently projected onto the camera coordinates to categorize each of the images into the bridge component. The proposed approach is successfully validated using a local highway bridge, pointing the way for improved inspection of full-scale bridges.