AI assisted pothole detection and depth estimation
Bibliographic record
Abstract
AI-assisted engineering solutions integrated with commercial RGB sensors and computationally intensive Graphical Processing Units (GPUs) promise a low-cost solution, to prevent deterioration of premature pavement disintegration. Potholes a common pavement distress are a severe threat to road safety and demand time and cost-effective state-of-the-art technologies for road inspection and condition monitoring. An intelligent pavement pothole detection system is proposed in this study by modifying the single stage CNN architecture-RetinaNet to detect potholes and perform metrological studies using 3D vision. The photogrammetric technique of structure from motion based on image frames extracted from pavement video recordings is used to model the 3D point cloud structure of potholes to assess the severity of the detected potholes as a function of its depth and is integrated with the CNN based pothole detection system. High F1 scores on benchmark dataset with a high value of 0.98, validate the model’s performance. A mean error below 5% is obtained on the measured depths thus promising an intelligent and practical solution to be implemented as part of a potential pavement health assessment system for future practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".