Document Type
Article
Publication Date
3-28-2023
Publication Title
Infrastructures
Volume
8
Abstract
Ancillary structures are essential for highways’ safe operationality but are mainly prone to environmental corrosion. The traditional way of inspecting ancillary structures is manned inspection, which is laborious, time-consuming, and unsafe for inspectors. In this paper, a novel image processing technique was developed for autonomous corrosion detection of in-service ancillary structures. The authors successfully leveraged corrosion features in the YCbCr color space as an alternative to the conventional red–green–blue (RGB) color space. The proposed method included a preprocessing operation including contrast adjustment, histogram equalization, adaptive histogram equalization, and optimum value determination of brightness. The effect of preprocessing was evaluated against a semantically segmented ground truth as a set of pixel-level annotated images. The false detection rate was higher in Otsu than in the global threshold method; therefore, the preprocessed images were converted to binary using the global threshold value. Finally, an average accuracy and true positive rate of 90% and 70%, respectively, were achieved for corrosion prediction in the YCbCr color space.
Issue
4
First Page
66
DOI
10.3390/infrastructures8040066
ISSN
2412-3811
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Amrita Das, Eberechi Ichi, and Sattar Dorafshan. "Image-Based Corrosion Detection in Ancillary Structures" (2023). Civil Engineering Faculty Publications. 8.
https://commons.und.edu/cie-fac/8