Performance Analysis of Thermographic Cameras Applied to Wood Damage Detection
Bibliographic record
Abstract
Wood is a crucial component of the green economy of the 21st Century. From house construction to innovative daily applications and products, wood is one of the most sustainable resources. However, as a natural material, it suffers deterioration with time. Infrared thermography may provide an excellent potential for detecting internal damage. Although the prices of infrared cameras have dropped recently, getting the best value for money and choosing the right camera for wood inspection is a significant challenge. Before choosing an infrared camera, the operator needs to consider several parameters, such as the temperature range, spectral range, thermal sensitivity, resolution, spatial resolution, accuracy, optics and focus, to make an informed decision. This study aims to evaluate the performance of two infrared cameras, a high-end model and a mid-range model, in visual wood damage detection. For this purpose, samples of different wood species with induced damage were observed using active thermography. Our results suggest that, for technical purposes such as qualitative studies, resolution and thermal sensitivity may be more important parameters than accuracy. The results achieved are an important contribution when deciding which infrared camera to purchase
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".