Image-Based Corrosion Detection in Ancillary Structures

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.

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