Viola-Jones Algorithm for Automatic Detection of Hyperbolic Regions in GPR Profiles of Bridge Decks
Bibliographic record
Abstract
Ground Penetrating Radar (GPR) is widely utilized as a Non-destructive technique by transportation authorities for inspection of bridge decks due to its ability to identify major subsurface defects in a short span of time. The attenuation of recorded signal at rebar level form a characteristic hyperbolic shape in profiles obtained from GPR scans and corresponds to the corrosiveness state of concrete. The detection of these hyperbolic regions is of paramount importance and is a precursor to successful interpretation of GPR data. This paper aims to automate the detection of hyperbolic regions or hyperbolas in GPR profiles based on Viola-Jones Algorithm. A custom detector is obtained through training with numerous samples of hyperbolas over multiple stages. The detection is achieved through the developed detector and it was applied over a complete bridge deck for validation purpose. The eventual goal of such detection is to facilitate the automation of GPR data analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".