Unsupervised defect segmentation with pose priors
Bibliographic record
Abstract
Single shot, semantic bounding box detectors, trained in a supervised manner are popular in computer vision-aided visual inspections. These methods have several key limitations: (1) bounding boxes capture too much background, especially when images experience perspective transformation; (2) insufficient domain-specific data and cost to label; and (3) redundant or incorrect detection results on videos or multi-frame data; where it is a nontrivial task to select the best detection and check for outliers. Recent developments in commercial augmented reality and robotic hardware can be leveraged to support inspection tasks. A common capability of the previous is the ability to obtain image sequences and camera poses. In this work, the authors leverage pose information as “prior” to address the limitations of existing supervised learned, single-shot, semantic detectors for the application of visual inspection. The authors propose an unsupervised semantic segmentation method (USP), based on unsupervised learning for image segmentation inspired by differentiable feature clustering coupled with a novel outlier rejection and stochastic consensus mechanism for mask refinement. USP was experimentally validated for a spalling quantification task using a mixed reality headset (Microsoft HoloLens 2). Also, a sensitivity study was conducted to evaluate the performance of USP under environmental or operational variations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".