Leveraging UAV Imagery and Deep Learning for Automated Object Detection
Bibliographic record
Abstract
India is one of the leading countries in rapid global infrastructure development.Road infrastructure is one of the major contributors to the same.This raises a need for the realtime maintenance of the developed infrastructure.In maintenance, precise identification and management of potholes are important, considering the safety of citizens.The current study presents a geo-intelligent framework for real-time detection of potholes.It uses advanced deep learning techniques such as PSP-Net and U-Net for pothole detection.It employs high-resolution unmanned aerial vehicle (UAV) imagery, digital surface model (DSM), along with training samples identified through annotations for model training and evaluation.Experimental results show that U-Net outperforms PSP-Net with an F1-score of 0.78, demonstrating high precision in pothole determination.This novel framework is further deployed in the form of a toolkit in the ESRI ArcGIS ecosystem.The two tools developed using the Python API were deployed for the determination of pothole volume and fill quantity estimation, respectively.The American Concrete Institute (ACI) approach was used to estimate the amount of repair materials needed for the identified potholes.The study helps in the reduction of man-hour efforts needed for lengthy field surveys for pothole identification.The Geo-Image Analytics toolbox offers a scalable solution for evolving urban infrastructure needs, marking a significant step forward in modernizing pothole management practices and the sustainability of the road infrastructure.
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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 | 0.001 |
| 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".