Semi-supervised pipe video temporal defect interval localization
Bibliographic record
Abstract
In sewer pipe closed-circuit television inspection, accurate temporal defect localization is essential for effective pipe assessment. Industry standards typically do not require time interval annotations, which are more informative but lead to additional costs for fully supervised methods. Additionally, differences in scene types and camera motion patterns between pipe inspections and temporal action localization (TAL) hinder the effective transfer of point-supervised TAL methods. Therefore, this study presents a semi-supervised multi-prototype-based method incorporating visual odometry for enhanced attention guidance (PipeSPO). The semi-supervised multi-prototype-based method effectively leverages both unlabeled data and time-point annotations, which enhances performance and reduces annotation costs. Meanwhile, visual odometry features exploit the camera's unique motion patterns in pipe videos, offering additional insights to inform the model. Experiments on real-world datasets demonstrate that PipeSPO achieves 41.89% AP across intersection over union thresholds of 0.1–0.7, improving by 8.14% over current state-of-the-art methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".