A doubt–confirmation-based visual detection method for foreign object debris aided by assembly models
Bibliographic record
Abstract
Foreign object debris (FOD) impacts significantly on the quality control during product assembly because it usually causes product failure. The vision-based method as a nondestructive and efficient technology has become an important approach to FOD detection. However, it faces two important challenges: (1) inexhaustible types (almost any object can become FOD) and (2) unpredictable locations (FOD can appear almost anywhere on surface of a product). Therefore, this paper proposes an FOD visual detection method based on doubt–confirmation strategy and aided by assembly models. Firstly, a coarse-to-fine method is designed for feature extraction and registration to align the test image with the reference image. Then, to solve the unpredictable location problem, different types of suspected FOD are extracted from the test image by a combined method of supervision and nonsupervision. Finally, to solve the inexhaustible type problem, an image comparison method based on a Histogram of Line Direction Angle is proposed, and re-recognition rules of suspected FOD established to complete the final discrimination. Experiments are conducted on a product with complex shape, and the results demonstrate the effectiveness and efficiency of our approach.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".