Condition Assessment of Unpaved Roads Using Low-Cost Computer Vision–Based Solutions
Bibliographic record
Abstract
Unpaved roads are an important part of the road transportation system of many countries and they contribute to the accessibility of remote communities and businesses. Despite the importance of unpaved road networks on social and economic development of remote regions, research on semiautomated and automated assessment of these roads is limited. This paper proposes low-cost computer vision–based solutions for assessment of unpaved roads using two approaches: unmanned aerial vehicle (UAV) and participatory-based imaging methods. Both methods use deep neural network to process captured images and locate major road distresses, including potholes, rutting, and corrugations. In addition, a method is proposed to estimate the size of detected potholes in the UAV-captured video frames. Each of the proposed methods was evaluated using a set of experiments, which demonstrated promising performance in assessment of these infrastructure assets that are vital for reliable access of rural and remote communities.
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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".