Application of A Drone Camera in Detecting Road Surface Cracks: A UAE Testing Case Study
Bibliographic record
Abstract
This research aims to introduce DJI Phantom 4 Pro, and its process of calibration and image processing. To get accurate aerial photos, a camera calibration process has been applied to find the true parameters (e.g., focal length, format size, principal point, and lens distortion) of the camera that took the photographs. Results from the point of view of variance factor confirmed the reliability of this camera for photogrammetric work. This study also calculates the 3D coordinates of unknown points on the ground and added Photogrammetric Derivatives DEM and accurate Digital Orthophotos to check the road condition. It is difficult to precisely identify them using publicly accessible standard satellite imagery (including the three multispectral bands (G, R, NIR) with resolutions of 20 meters). Agisoft software has been used for image analysis and for accuracy checking to detect street cracks to provide recommendations and guidance to the related planning entity for maintenance. Captured aerial crack images were processed using Agisoft software, from which orthophotos were produced and potential cracks identified. Moreover, the study aims to achieve optimal quality in a road 3D model using MMS and to determine the number of control points or sections needed to create an accurate 3D model of the road to be used as initial data information in the rehabilitation process by detecting the cracks in the roads.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".