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Abstract
Corrosion detection is crucial due to its significant economic impact. This paper presents a deep learning model for pixel-level corrosion segmentation, incorporating Bayesian methods to provide confidence estimates. Experiments on a newly collected dataset show promising results, exceeding existing state-of-the-art accuracy while offering uncertainty measures to improve decision-making.
Publisher
npj Materials Degradation
Published On
Mar 31, 2022
Authors
Will Nash, Liang Zheng, Nick Birbilis
Tags
corrosion detection
deep learning
pixel-level segmentation
Bayesian methods
confidence estimates
uncertainty measures
economic impact
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