Automated detection of anomalies in sewer closed circuit television videos using proportional data modeling
Bibliographic record
Abstract
Sewer pipeline condition information is usually collected using closed circuit television (CCTV). Moreover, in order to evaluate the condition of pipeline, data should be processed by a certified operator, which is time consuming, costly, and error prone due to operator's skillfulness or fatigue. Automating the detection of anomalies can reduce time and cost of inspection while ensuring the accuracy and quality of assessment. However, considering various types of defects in sewer pipelines and numerous patterns of each, it seems to be difficult to detect the defects using computer vision techniques. This paper presents an efficient anomaly detection algorithm to support automated detection of sewer defects from data obtained from CCTV inspection videos. In this model Hidden Markov Model (HMM) for proportional data modeling is employed theoretically and its performance of anomaly detection in an example of sewer CCTV videos has been assessed. The algorithm consists of modeling conditions considered as normal and detecting outliers to this model.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".