A Learning-Based Approach for Automatic Defect Detection in Textile Images
Bibliographic record
Abstract
This paper addresses the textile defect detection problem using a machine-learning approach. We propose a novel algorithm that uses supervised learning to classify textile textures in defect and non-defect classes based on suitable feature extraction and classification. We use statistical modeling of multi-scale contourlet image decomposition to obtain compact and accurate signatures for texture description. Our defect detection algorithm is based on two phases. In the first phase, using a training set of images, we extract reference defect-free signatures for each textile category. Then, we use the Bayes classifier (BC) to learn signatures of defected and non-defected classes. In the second phase, defects are detected on new images using the trained BC and an appropriate decomposition of images into blocks. Our algorithm has the capability to achieve highly accurate defect detection and localisation in textile textures while ensuring an efficient computational time. Compared to recent state-of-the-art methods, our algorithm has yielded better results on the standard TILDA database.
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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 | 0.002 |
| 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".