Computer Vision for Infrastructure Health Monitoring: Automated Detection of Pavement Rutting from Street-Level Images
Bibliographic record
Abstract
The increasing availability of low-cost sensors, such as smartphones and the cameras embedded in cars for driving assistance, provides new opportunities to enhance pavement health monitoring. While computer vision techniques have been successfully used to detect pavement distresses using 2D images, distresses involving depth measurements, such as rutting, remain a challenge. The objective of this study is to develop an object detection model to automatically detect instances of rutting in pavements. This study leverages pavement images and distress data collected from the United States Federal Highway Administration Long-Term Pavement Performance (LTPP) database to train a rutting detection model. Transfer learning is used to analyze the generalizability of the model. Results indicate that street-level images are highly suitable for detecting pavement rutting using computer vision algorithms. Future research is needed to quantify the severity of rutting using image processing techniques.
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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".