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27 July 2019 Segmenting localized corrosion from rust-removed metallic surface with deep learning algorithm
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Abstract

We present a segmentation process for detecting localized corrosion on rust-removed metallic surface based on a deep learning algorithm. In the proposed process, first the corrosion images are enhanced by a preprocessing technique, then a patch extraction process is used to collect the image set to train the deep learning model, which is used to segment the target image later. Finally, the segmentation result is improved by a postprocessing method. The performance of the segmentation process is verified with classification indicators and the receiver operating characteristic curve. The results show that the proposed method is effective in identifying localized corrosion from the metallic background, which can provide an accurate quantitative analysis tool to study the corrosion behavior.

© 2019 SPIE and IS&T 1017-9909/2019/$28.00 © 2019 SPIE and IS&T
Shouxin Zhang, Zili Li, Chao Yang, and Chengyuan Zhu "Segmenting localized corrosion from rust-removed metallic surface with deep learning algorithm," Journal of Electronic Imaging 28(4), 043019 (27 July 2019). https://doi.org/10.1117/1.JEI.28.4.043019
Received: 17 February 2019; Accepted: 3 July 2019; Published: 27 July 2019
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