A Deep Learning-Based Approach for Pipeline Cracks Monitoring
Bibliographic record
Abstract
In order to improve the efficiency and accuracy of pipeline surface cracks monitoring based on image processing, the Convolutional Neural Network (CNN) algorithm in target detection is introduced to quickly identify the type, location, and area for the extracted cracks area with borders, the CNN based on crack contour network (CCN) method used to locate and extract the crack shape. CCN algorithm introduces the accuracy rate (P%), recall rate (R%), and F-score (F%) index to evaluate the algorithm in the problem during cracks monitoring, and determines the corresponding contour area of the crack frame according to the maximum F-score. A pipeline image was carried out by using an inspection drone with high definition camera. The results show the recognition efficiency and accuracy of the proposed method. After the optimal value of the degree threshold, the accuracy rate, recall rate, and F-score are recorded 91. 8%, 86. 1%, and 84.6%, respectively.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".