Multi-scale signal-to-noise driven fusion of post-processing sequences for enhanced defect detectability in active infrared thermography
Bibliographic record
Abstract
Active infrared thermography has emerged as a crucial tool in non-destructive testing, providing real-time visual representations of thermal patterns on material surfaces. However, detecting and analyzing defects can be challenging due to noise interference, the lack of standardization in post-processing techniques, complexity in data analysis, variability in defect visibility across frames, and the influence of environmental factors. To address these limitations, this study proposes a novel approach that enhances defect detectability by fusing multiple sequences derived from various post-processing methods into single, interpretable images. The proposed approach employs a multi-scale signal-to-noise ratio metric to accurately identify regions of interest and determine the optimal time at which defect detectability is maximized. Validation with two composite specimens featuring diverse defect characteristics demonstrates the capability of the method to simplify analysis and reliably improve detection performance. Compared with wavelet-based image fusion, the proposed approach achieves superior defect visibility and clarity, demonstrating a significant advancement in the effectiveness and reliability of thermographic inspections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".