Deep Learning-Enhanced Push-Broom Hyperspectral Imaging System for Non-Destructive Subsurface Defect Detection
Bibliographic record
Abstract
This study presents a custom-developed push-broom hyperspectral imaging (HSI) system integrating a hyperspectral camera, halogen illumination, and a motorized linear translation stage with a C\textbackslash ＃－based interface. The system achieves a spatial resolution of$31.9 \mu ~\mathrm{m} / \text{pixel}$（vertical）and$140.4 \mu ~\mathrm{m} / \text{pixel}$(scan direction)with a spectral range from 367 to 1027 nm and a spectral resolution of 2.87 nm at 435.68 nm ．A 3D hyperspectral data is processed by selecting diagnostically relevant wavelengths to reconstruct 2D perspective images. Deep learning and Gaussian Mixture Models are used for unsupervised feature extraction and defect classification. Experimental results demonstrate the system's ability to detect surface and subsurface structures, including concealed playing card patterns and internal cracks in the laser－annealed silicon carbide substrate, highlighting its potential for automated，non－ destructive internal defect inspection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".