Automated Sewer Pipeline Inspection Using Computer Vision Techniques
Bibliographic record
Abstract
To facilitate condition assessment in sewer pipeline networks current practice is using the available technologies to visually inspect the internal condition of pipelines. Closed circuit television (CCTV) has been one of the most used methods in North American municipalities in last decades. However, this method requires hours of videos to be inspected by certified inspectors which is time consuming, labor intensive, and error prone. The main objective of this research is to propose an automated approach for inspection and condition assessment of sewer pipelines using computer vision techniques. This research includes two main part: identifying region of interest (ROI) in sewer inspection videos which are most likely to contain sewer defects, and defect detection and classification among the identified anomalous frames. The ROI detection model employs proportional data modeling using hidden Markov models (HMM) to extract abnormal frames from sewer CCTV videos. In the next step, a deep learning approach using convolutional neural networks (CNN) is proposed to detect the defects and classify them. The presented algorithm has been developed and tested using the data sets from CCTV inspection reports.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".