Mean Profile Depth of Pavement Surface Macrotexture Using Photometric Stereo Techniques
Bibliographic record
Abstract
Pavement surface texture affects many vehicle and road characteristics; therefore, efforts are needed to develop more advanced techniques for evaluating pavement texture. Photometric stereo techniques are widely used to recover three-dimensional shapes of objects and can also be used to recover surface texture. A four-source photometric stereo system for recovering pavement surface texture is proposed. Five types of pavement surfaces were tested to validate the system. Mean profile depths computed from the recovered surface were compared with those measured manually by using a depth dial gauge. The sensitivity of the technique to illumination angle was studied for five zenith angles: σ=26 , 28, 30, 32, and 34°. The computed mean profile depths from the proposed system are linearly correlated with those from the depth dial gauge with coefficients of determination ranging from 0.82 at σ=34° to 0.92 at σ=30° . Tests showed that the proposed system can be used to recover pavement surface heights and to estimate the mean profile depth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".