Defects detection in infrared thermography by deep learning algorithm
Bibliographic record
Abstract
Non-destructive Evaluation (NDE) is a field that is used to identify all kinds of structural damage in an object of interest without resulting in any permanent damage or modification to the object. This field has been intensively investigated for many years. Among several research topics in this field, the supervised defect detection methods are among the most innovative and challenging. In recent years, the deep learning field of artificial intelligence has made remarkable progress in image processing applications. Deep learning has shown its ability to overcome most of the disadvantages suffered by previous existing approaches in a great number of applications. In this paper, we propose a deep learning architecture based on infrared thermography inspection intended to automatically identify defects (including internal and invisible cracks, delamination, etc.) efficiently and accurately. We studied the proposed deep learning algorithms to achieve automatic defect detection and precise localization (subsurface defects case) from different thermal image sequences. To evaluate the efficiency and robustness of the proposed methodology, specimens containing artificial defects were selected for experimental configuration.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".