Real-Time Defect Detection in Sewer Closed Circuit Television Inspection Videos
Bibliographic record
Abstract
Closed circuit television (CCTV) is the most employed technology in inspection of sewer pipelines by municipalities in North America. Generally, visual inspection of sewer pipelines is done manually by a certified operator which is time-consuming, costly, and error-prone due to the operator’s experience or fatigue. Automating the detection of anomalies can reduce time and cost of inspection while ensuring accuracy and quality of assessment. However, various types of defects in sewer pipelines and numerous patterns of each, make it difficult to detect the defects using computer vision techniques. This paper proposes an innovative and efficient anomaly detection algorithm to provide automated detection of sewer defects from data obtained from CCTV inspection videos. The algorithm employs Hidden Markov Model (HMM) for proportional data modeling. The algorithm performs real-time anomaly detection and localization and consists of modeling conditions considered as normal and detecting outliers to this model. The proposed model is tested on videos from sewer inspection report of the City of Laval, to evaluate its performance in anomaly detection in sewer CCTV videos.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".