Automatic Visual Defect Detection Using Texture Prior and Low-Rank Representation
Bibliographic record
Abstract
Automatic surface detection for quality control has largely employed image processing techniques, for example in steel and fabric defect inspection. There are rising demands in the quality control industry for defective image analysis to fulfill its vital role in visual inspection. In this paper, we introduce an unsupervised method using a low-rank representation based on texture prior for detection of defects on natural surfaces and formulate the detection process as a novel weighted low-rank reconstruction model. The first step of the proposed method estimates the texture prior to a given image by constructing a texture prior map where higher values indicate a higher probability of abnormality. The second step of the proposed method detects the defect via low-rank decomposition with the help of the texture prior. Experiments on synthetic and real images show that the proposed method is superior in terms of detection accuracy and competitive in computational efficiency with respect to the state-of-the-art methods in surface defect detection research. This contribution is of particular interest for manufacturers (e.g., steel and fabric) for which defect detection largely relies on manual 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".