Small Parts Classification with Flexible Machine Vision and a Hybrid Classifier
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
A Flexible Machine Vision (FMV) Inspection System has been developed that requires minimal retuning in hardware and software as applications are changed up. The flexibility of the system was evaluated by applying it to an inspection problem with three different types of small parts: plastic gears, plastic connectors and metallic coins, with minimal retuning when moving from one application to the others. The system was required to differentiate between 4 different known styles of each part plus one unknown style, for a total of 5 classes. In previous work, a hybrid Support Vector Machine (SVM) classifier was developed for the connector application. When applied to the coin application, the hybrid SVM could not achieve the target performance of 95% accuracy. A new hybrid that method that combines SVM and an Artificial Neural Network (ANN) or ANN-SVM classifier was subsequently developed to overcome this problem and the results are presented in this paper. The image library used in this study is available at http://my.me.queensu.ca/People/Surgenor/Laboratory/Database.html.
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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