ROI-driven thermal hyperplane analysis for automated non-destructive evaluation via pulsed thermography
Bibliographic record
Abstract
This paper proposes a novel methodology for structural fault detection utilising pulsed infrared thermography data. The approach systematically scans thermal image sequences using Regions of Interest (ROIs) with variable sizes, adjusted according to the expected fault dimensions. All temporal frames are considered during the analysis. For each ROI, a transformation is performed to linearise the thermal response, followed by a reconstruction of the data in a flattened space combining spatial coordinates, time, and temperature. These reconstructed hyperplanes are subsequently evaluated by a Convolutional Neural Network to classify the presence or absence of faults. Experimental validation demonstrates that the proposed method achieves a fault detection accuracy of 96%, with only one false positive identified. The results highlight the method’s potential for enhancing the reliability and automation of structural health monitoring systems using infrared thermography. • ROI-ThermNet: Novel fault detection via variable-sized ROI scanning. • Achieved 96% fault detection accuracy with only 1 false positive. • No manual preprocessing; uses raw thermal data directly. • Flexible CNN accepts variable-sized ROIs without retraining. • Full temporal data in ROIs boosts NDE automation and application generalization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".