Automated Detection and Quantification of Sewer Pipe Cracks Using a CNN-Based Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Field inspection of sewer pipes was commonly conducted with closed-circuit television (CCTV); however, CCTV images are mostly interpreted manually, which is time-consuming and has low accuracy. To address this issue, a convolutional neural networks-based (CNN-based) algorithm for automated detection and quantification of cracks in urban sewer pipes from CCTV images was proposed, named “crack detection and quantification algorithm” (CDQ). The CDQ algorithm contains a crack detection model based on an improved feature transmission mechanism, and a novel pixel-level crack quantification algorithm developed through geometric transformation. The CDQ algorithm was trained and validated with real CCTV images. From the results, the CDQ algorithm exhibited excellent performance in crack detection and a mean average precision of 83% in crack quantification, outperforming existing models. The present case study provides an efficient method for pipe defect detection and quantification from CCTV images and contributes to the development of smart inspection of sewer pipes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it