Dataset for Drone-based Inspection of Road Pavement Structures for Cracks
Bibliographic record
Abstract
The dataset is available online as a benchmarking dataset for drone-based inspection of pavement structures. The data were acquired in an experimental road with a length of 386 meters, belonging to Montmorency Forest laboratory of Université Laval, located in North of Quebec City, on 2021/06/16. The road is mainly used for testing pavement paints, laying techniques, and inspection simulations. A DJI MINI 2 drone was employed to collect images for this dataset. The drone has a 12 megapixels camera with an 83 degrees field of view, capturing 1920 x 1080 images in the continuous recording mode. The drone performs six passes on an experimental road at different altitudes and horizontal speeds. The drone was controlled manually, and the footage was acquired using the embedded camera that stabilized and controlled using the drone's gimbal. Moreover, after data acquisition, the length and width of some cracks and road landmarks were measured for evaluating crack characterization. **** In case of any use, please cite this dataset and our paper ****
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 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".