AI-Driven System For Detecting And Mapping Potholes
Bibliographic record
Abstract
The increase in road accidents due to poor road conditions indicates the critical importance of timely maintenance to ensure public safety, reduce vehicle damage, and minimize costly repairs. The project presents an AI-driven system designed to detect and map road potholes, assess the severity of the pothole, and analyze road quality over time using video visuals. Utilizing advanced machine learning techniques and image processing algorithms, the system processes video data captured from cameras mounted on vehicles. The AI model accurately identifies potholes, classifies their severity, and geo-tags them on a digital map, providing real-time updates for drivers, public, and road maintenance authorities. By automating the detection and classification processes, the system significantly reduces the time and labor required for manual inspections. Extensive testing with real-world video footage highlights the system’s reliability and adaptability across various road environments, ensuring that it can be deployed in diverse geographic locations. By providing an understanding of road conditions, the system enables precise maintenance strategies and long-term planning, ultimately extending the lifespan of road infrastructure and enhancing overall transportation safety and efficiency.
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 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".