Automated pothole condition assessment in pavement using photogrammetry-assisted convolutional neural network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automated detection of pavement distress can prevent deterioration of premature surface disintegration in pavements. Potholes that are a common sight in harsh and cold terrains are a severe threat to road safety and a major contributing factor to pavement distress. To facilitate timely detection and repair of potholes, a computationally light and feasible, intelligent pavement pothole detection system is proposed by developing a novel workflow for image-based detection and severity assessment. A single-stage CNN architecture, RetinaNet is modified and optimised to best detect potholes and used in combination with a novel pothole depth estimation algorithm. A comparative evaluation of the model’s performance against the existing state-of-the-art model on the benchmark dataset establishes the proposed model’s high performance and applicability in real-time scenarios. The depth estimation algorithm is based on a 3D road surface model generated by employing the photogrammetric process of structure from motion (SfM). The point cloud data obtained thereafter, is used for accurate measurement of pothole depth. The comparison of the derived depth with the onsite depth measurement of the pothole reveals a mean error below 5%. This method leads to a practical and intelligent solution to be implemented as part of a potential pavement health assessment system for future practice.
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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 | 0.001 | 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 it