An Automatic Image Recognition System for Winter Road Condition Monitoring
Bibliographic record
Abstract
Municipalities and contractors in Canada and other parts of the world rely on road \nsurface condition information during and after a snow storm to optimize maintenance operations \nand planning. With an ever increasing demand for safer and more sustainable road \nnetwork there is an ever increasing demand for more reliable, accurate and up-to-date road \nsurface condition information while working with the limited available resources. Such high \ndependence on road condition information is driving more and more attention towards analyzing \nthe reliability of current technology as well as developing new and more innovative \nmethods for monitoring road surface condition. This research provides an overview of the \nvarious road condition monitoring technologies in use today. A new machine vision based \nmobile road surface condition monitoring system is proposed which has the potential to \nproduce high spatial and temporal coverage. The proposed approach uses multiple models \ncalibrated according to local pavement color and environmental conditions potentially \nproviding better accuracy compared to a single model for all conditions. Once fully developed, \nthis system could potentially provide intermediate data between the more reliable \n xed monitoring stations, enabling the authorities with a wider coverage without a heavy \nextra cost. The up to date information could be used to better plan maintenance strategies \nand thus minimizing salt use and maintenance costs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".