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.
