Performance Evaluation of Sensor- and Image-Based Technologies for Automated Pavement Condition Surveys
Bibliographic record
Abstract
Even though companies that assess pavement condition compete to innovate by providing better software for automatic analysis and diagnosis, the industry as a whole remains limited, and data collection and storage methods are disparate. In fact, software and handling procedures are proprietary—each vendor has its own automated technology to detect, classify, and quantify surface distresses. In a research effort sponsored by the Ministry of Transportation of Ontario, Canada, the performance of sensor- and image-based pavement condition assessment was compared. First, a data management plan was created to allow efficient data manipulation. Second, a suitable set of similar distresses was selected as response variables of interest to design and conduct statistical experiments. Third, advanced analysis of variance was performed to allow statistical data comparisons among companies and among automated technologies. Finally, results were discussed and recommendations made. Overall, service provider measurements using sensor-based equipment showed no significant differences; however, those taken with digital image technology did. The implications of such outcomes, including implementation details to encourage practitioners to benefit from these preliminary results, are discussed. More broadly, road agencies are given an opportunity to revisit selection decisions concerning the acceptance or rejection of pavement data collected by a range of contractors.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".