Efficient object classification using multiple views in manufacturing environments
Bibliographic record
Abstract
In this paper we present a framework for rapid object classification that uses multiple views to classify visually similar automotive parts on a conveyor belt. We have constructed a dataset of 25 different manufactured parts consisting of window pillars and guides. These parts vary in size, orientation and luster and are captured from four view points. We are able to achieve a classification rate of 97.4% using our dataset. Using an object localization approach for each view we provide a method that reduces the time it takes to build the visual vocabularies by 36.6%. Our framework shows has an improved accuracy over using single view classification and is fast enough to have practical industrial applications for fine-grained classification of very similar objects. We are able to demonstrate that ORB descriptors provide superior performance and speed over SIFT and SURF descriptors. By harnessing the speed and low computational expense of using ORB features with our framework, we are able to show that our approach has practical industrial applications in improving quality control.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".