An Industrial Application Towards Classification and Optimization of Multi-Class Tile Surface Defects Based on Geometric and Wavelet Features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is possible to detect visual surface defects with software in industrial tile production and increase productivity by automating the quality control process. In this process, low error rate and low cost are important indicators. In order to eliminate this negativity and the effect of the human factor, error detection software has been developed in an artificial intelligence-based industrial artificial vision environment. Spots, scratches, cracks, pore defects, which are the most common surface defects, are classified according to 6 different geometric and wavelet transform attributes. Firstly, an industrial artificial vision environment was created. In this environment, a total of 150 tile images, equal numbers from each class, were obtained on the real-time production line. The resulting images were converted into binary images by preprocessing and filtering. For classification, the support vector machines method, which performs high in two-class classifications, is used with the one versus all approach. In classifications made using RBF kernel function using wavelet features as classification performance, a higher success was achieved in all defect classes than geometric features. Real-time application software for all these processes has been developed with the Python language on Ubuntu operating system.
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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 | 0.000 | 0.000 |
| 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