ROI-driven thermal hyperplane analysis for automated non-destructive evaluation via pulsed thermography
Why this work is in the frame
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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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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 | 0.001 | 0.004 |
| 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 it