Quantification of pitting corrosion from thermography using deep neural networks
Bibliographic record
Abstract
Pipelines serve as the major infrastructure for transmission and distribution in oil and gas and water industries. The health condition of a metallic pipe is determined mainly by the extent to which it is corroded. Thus, the quantification of pitting corrosion in terms of metal loss is required for the understanding of pipe condition. There are different ways to quantify corrosion pit geometry. Direct methods measure the pit depth of pipe samples, which are sand/grit blasted to remove corrosion products, and are often adopted in the laboratory. Indirect methods employ non-destructive inspection techniques to detect and quantify the corrosion without sandblasting, which is preferred for a field test. In this study, pulsed thermography is considered for the quantification of pitting corrosion in a metallic pipe. Thermography testing can generate a sequence of infrared images, which reflect the diffusion process of heat through the pipe wall. This paper proposes a new deep neural network-based approach to quantify the pitting corrosion damages from the acquired thermography images. Through extensive experimental tests, the quantified results demonstrate a reasonably good linear relationship with metal loss of pipe, as the quantification accuracy was better than 98%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".