Toward low-cost 3D automatic pavement distress surveying: the close range photogrammetry approach
Bibliographic record
Abstract
The management of road networks requires accurate information on surface condition. Automated methods have been developed to collect road surface data, based on digital imaging systems with or without laser-profilers. While these represent state-of-the-art technology, the equipment is expensive and there are issues on accuracy and robustness. In a broad sense, the issues extend to whether user needs can be met with alternative, less-expensive technology, and whether this can be accomplished with modifications and (or) integrating a new technology with the existing equipment. This paper addresses the foregoing issues as a fundamental research and development question. It suggests that photogrammetric techniques have the potential to provide a unique and practical answer to the question. An overview and detailed formulation of the photogrammetric technology is described, with examples from experiments on road surfaces. The technical and economic advantages of the new technology are pointed out, including it...
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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 | 0.001 | 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) | 0.001 | 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".