Three-dimensional non-destructive testing (NDT) in the infrared spectrum
Bibliographic record
Abstract
Three-dimensional (3D) vision scanning for metrology and inspection applications is an area that knows an increasing interest in the industry. This interest is driven by the recent advances in 3D technologies, permitting to attain high precision measurements at an affordable cost. 3D vision allows for the modelling and inspection of the visible surface of objects. When it is necessary to detect subsurface defects, active infrared (IR) thermography is one of the most used tools today for non-destructive testing (NDT) of materials. Fusion of these two modalities allows the simultaneous detection of surface and subsurface defects and to visualize these defects overlaid on a 3D model of the scanned and modelled parts or their 3D computer-aided design (CAD) models. In this work, we present a framework for automatically fusing 3D data (scanned or CAD) with the infrared thermal images for an NDT process in 3D space.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".