Surface defect visualisation using scanning electromagnetic induction thermography
Bibliographic record
Abstract
Scanning electromagnetic induction thermography (SEIT) has great potential for industrial applications, such as rapid visualising of surface cracks. This dynamic non-destructive testing method has improved detection efficiency but has led to blurred or discontinuous defect boundaries at varying scanning speeds. To alleviate this problem, we explore the use of the patch-based sparse decomposition (PSD) technique. This technique improves dynamic imaging by retaining only the information associated with sparse regression and introducing a locally adaptive threshold. Besides, this method offers a potential thermal image preconditioned scheme for follow-up artificial intelligence detection. Experimental results show that PSD can correct blurred images caused by relative motion, thus improving defect detection accuracy. Moreover, it is suitable for both translational and rotational detection systems. Images reconstructed through the deblurring process demonstrate the ability to visualise holes with a radius of 0.5 mm at velocities up to 150 mm/s, while cracks with a defect width of 0.2 mm on a railroad wheel can be detected at speeds ranging from 25 mm/s to 75 mm/s.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".