A Support Vector Machine Based Online Learning Approach for Automated Visual Inspection
Bibliographic record
Abstract
In manufacturing industry there is a need for an adaptable automated visual inspection (AVI) system that can be used for different inspection tasks under different operation condition without requiring excessive retuning or retraining. This paper proposes an adaptable AVI scheme using an efficient and effective online learning approach. The AVI scheme uses a novel inspection model that consists of the two sub-models for localization and verification. In the AVI scheme, the region localization module is implemented by using a template-matching technique to locate the subject to be inspected based on the localization sub-mode. The defect detection module is realized by using the representative features obtained from the feature extraction module and executing the verification sub-model built in the model training module. A support vector machine (SVM) based online learning algorithm is proposed for training and updating the verification sub-model. In the case studies, the adaptable AVI scheme demonstrated its promising performances with respect to the training efficiency and inspection accuracy. The expected outcome of this research will be beneficial to the manufacturing industry.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".