An adaptive machine learning framework for multi-scenes road surface weather condition monitoring
Bibliographic record
Abstract
Timely road surface condition (RSC) monitoring and maintenance significantly influences road safety. The current RSC relies on fixed road surveillance cameras and in-vehicle cameras. However, the fixed camera demands higher precision, while the in-vehicle camera requires higher timeliness. To address these challenges, this paper introduces an adaptive machine learning framework for simultaneous road surface detection on both device types. Initially, a convolutional neural network-based differentiation module identifies image sources. Subsequently, an adaptive algorithm switching mechanism leads to the development of two algorithms improved upon the real-time object detection algorithms. At last, extensive experiments with datasets collected from Ontario, Canada and Iowa, U.S.A. validate the framework. Results show satisfactory classification accuracy, detection precision, and speed. Notably, the mean average precision, namely mean of the average precision for all categories (mAP), reaches 91.9% for fixed cameras and 90.6% for in-vehicle cameras, outperforming existing road surface snow detection models.
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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 | 0.000 | 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 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".