Machine Learning–Based Smart Road Surface Anomaly Detection
Bibliographic record
Abstract
Infrastructure maintenance and road safety are the key elements behind transportation systems to ensure efficiency and safety towards the commuting system. Accidents, vehicle damages, and increased transport costs are all results created by potholes and humps which eventually act as major catalysts to road users. This project tries to resolve this by developing an automated pothole and hump detection system using machine learning and computer vision techniques. The system uses a deep learning-based approach trained on the Roboflow pothole dataset to analyze any road images and detect abnormalities with accuracy. The proposed solution is a real-time implementation and is flexible enough to be used on various types of road conditions, lighting environments, rainy conditions, and average traffic density. The system utilizes the advanced computer vision algorithms to detect unsafe road conditions and give input into actionable maintenance at the right time. Furthermore, the system's scalability increases with cloud-based interfacing, which makes it deployable under smart city infrastructure or standalone in drones or vehicles. Dynamic adaptability, detection accuracy, and seamless integration into existing road monitoring frameworks are few features that stand out within the framework. This would make the road safe, cost-effective, and avoid delays in hazard management while improving maintenance workflows. The improvement of roads would allow safer commuting patterns, better planning in the city's infrastructure, and smarter development in infrastructures while reinforcing artificial intelligence to build sustainable cities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".