Deep learning corrosion detection with confidence
Source abstract
Abstract
Abstract Corrosion costs an estimated 3–4% of GDP for most nations each year, leading to significant loss of assets. Research regarding automatic corrosion detection is ongoing, with recent progress leveraging advances in deep learning. Studies are hindered however, by the lack of a publicly available dataset. Thus, corrosion detection models use locally produced datasets suitable for the immediate conditions, but are unable to produce generalized models for corrosion detection. The corrosion detection model algorithms will output a considerable number of false positives and false negatives when challenged in the field. In this paper, we present a deep learning corrosion detector that performs pixel-level segmentation of corrosion. Moreover, three Bayesian variants are presented that provide uncertainty estimates depicting the confidence levels at each pixel, to better inform decision makers. Experiments were performed on a freshly collected dataset consisting of 225 images, discussed and validated herein.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Non-Destructive Testing Techniques · Corrosion Behavior and Inhibition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
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