Viola-Jones Algorithm for Automatic Detection of Hyperbolic Regions in GPR Profiles of Bridge Decks
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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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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 | 0.000 | 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