Creating a First-Pass Algorithm for Corrosion Assessment in Bridge Inspections Using Machine Learning and UAV-Collected Imagery Data
Herndon, H., and Tien, I., “Creating a First-Pass Algorithm for Corrosion Assessment in Bridge Inspections Using Machine Learning and UAV-Collected Imagery Data,” Structure and Infrastructure Engineering, pp. 1-15, 2025
Abstract — Unmanned aerial vehicles (UAVs) have the potential to reduce bridge inspection time and cost while increasing safety. However, UAV-collected field data has inherent properties that complicate damage assessment. In this article, the authors integrate UAV-collected imagery data with automatic defect detection to create a novel first-pass bridge inspection algorithm, which aims to conduct an initial corrosion assessment to determine if further inspection is needed. The authors use UAV-captured images of bridges near Atlanta, Georgia, USA, to create a dataset representative of bridge inspections, including the presence of chaos and misleading objects. The proposed methodology integrates deep learning methods (fully convolutional network (FCN)) to remove natural elements in the image background that resemble corrosion, image processing techniques to quantify texture and reduce lighting effects, and unsupervised learning (K-means) for corrosion segmentation. Experimental results show that the K-means algorithm outperforms other segmentation methods, including image thresholding and deep learning, with a recall of 0.78 and mIoU of 0.72 on UAV-collected field data. Thus, the newly developed method is a promising tool to improve the efficiency and safety of bridge inspections by reducing the number of full inspections conducted on structurally sound bridges.
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