New technologies, such as unmanned aerial vehicles (UAVs) and machine learning, have been used to conduct imagery-based bridge inspections and evaluate damage on bridges. However, corrosion detection is still an open problem, and corrosion detection algorithms have only proven adequate in certain environments and conditions. The main goal of this project is to explore the use of UAVs as a proof of concept to detect and characterize corrosion on bridges in Georgia. The first objective of this research is to investigate the use of UAVs for bridge inspections. The second objective is to develop and evaluate automatic corrosion detection and evaluation algorithms. As part of this project, imagery data from two bridges, selected in collaboration with Georgia Department of Transportation (GDOT) personnel, was collected using the Skydio 2+ drone. After the images were cleaned and labeled, they were used in varying computer vision and machine learning algorithms for corrosion detection. First, texture thresholding and color thresholding methods were implemented.
Bridge inspection is a critical task needed to monitor bridge quality and serviceability. In the U.S., 40 percent of bridges are more than 50 years ol...
This volume focuses on one of the key components of the IRSV system, i.e., the AMBIS module. This module serves as one ofthe tools used in this study ...
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