A Framework for Autonomous Inspection of Bridge Infrastructure using Uncrewed Aerial Vehicles
Bibliographic record
Abstract
Traditional bridge inspections are labor-intensive, time-consuming, and costly, relying on human inspectors for close visual and physical examinations, making them subjective, inaccurate, and non-repetitive. Leveraging Uncrewed Aerial Vehicles (UAVs) for UAV-enabled bridge inspection (UBI) has the potential to save over 50% of costs compared to traditional methods. However, existing UBI research lacks a unified, end-to end autonomous solution, often presenting isolated automation solutions at each stage of the inspection process. In this work, a comprehensive framework for autonomous bridge inspection using a single UAV is presented, encompassing mission planning, data acquisition, data analysis, and decision-making. Bridges were chosen as the focus of the framework due to their mandated inspection requirements compared to other infrastructure assets. We define the UBI system architecture, evaluate specific methods for each system element, and finally present how the UBI can be achieved through a phased procedure. Using physical experiments, the proposed procedure is validated with low-cost off-the-shelf hardware and software components, which demonstrates not only the feasibility but also the simplicity of the proposed framework using currently available technology. This research offers a comprehensive solution to revolutionize bridge maintenance, improve safety, reduce expenses, and streamline the inspection process for the longevity of critical transportation 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".