A Flexible Machine Vision System for Small Parts Inspection Based on a Hybrid SVM/ANN Approach
Bibliographic record
Abstract
The automated inspection and sorting of parts is a common application of Machine Vision (MV). The sorting of parts is possible only after reliable classification. The goal of this thesis was to develop and validate a flexible MV system that can reliably classify small parts. Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) are popular choices as classification algorithms. Classifiers developed from supervised algorithms perform well when trained for a specific application with known classes. Their drawback is that they are considered inflexible as they cannot be easily applied to a different application without extensive retuning. Moreover, for a given application, they do not perform properly if there are unknown classes. Classifiers developed from semi-unsupervised algorithms can work with unknown classes but cannot work with multiple known classes. A novel solution to these limitations has been developed using a hybrid two-layered approach with supervised SVMs, semi-unsupervised SVMs and supervised ANNs. With the hybrid approach as the basis for the classifier, a flexible MV system was designed with four key characteristics: 1) cost effective hardware, 2) a realistic and manageable image database, 3) an effective image conditioning process and 4) a comprehensive features library. Four hybrid classification methods were developed and tested: 1) semi-unsupervised SVM followed by supervised SVM (USVM-SSVM), 2) supervised SVM followed by semi-unsupervised SVM (SSVM-USVM), 3) semi-unsupervised SVM followed by supervised ANN (USVM-SANN) and 4) supervised ANN followed by semi-unsupervised SVM (SANN-USVM). The target performance criteria for the system was an accuracy of 95% with 0% false positives. To validate the system and to demonstrate its flexibility, experiments were conducted with two hardware setups, three applications (gears, connectors, coins) and five sets of high quality images with known/unknown classes. The effect of image quality was studied by digitally blurring and dimming the conditioned images. It was found that SANN-USVM gave the best results and exceeded the target performance criteria. A software package known as FlexMVS for Flexible Machine Vision System was written to evaluate the hybrid approach and to enable easy execution of the image conditioning, feature extraction and classification steps.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".