Decentralized operational modal identification using drone-mounted vision system via homography transformation
Bibliographic record
Abstract
Due to recent advancements in camera and mobile sensing platforms, drone-based structural inspection techniques have gained considerable interest in structural health monitoring. In particular, drone-based techniques provide promising alternatives for contactless and remote inspections for hard-to-reach areas at low cost. Although these drone-assisted image-based inspections have shown significant success for fast, safe, and cost-effective structural assessments, their application in vibration-based structural monitoring is still in the infancy stage. The majority of drone-assisted vibration monitoring remains limited to lab-scale experiments due to the challenges of accurately estimating drone motion in field conditions, with many limited to the extraction of natural frequencies. The primary challenge in estimating mode shapes is capturing phase information from low-amplitude vibration under noisy field conditions. This paper presents a contactless, drone-based vision methodology for decentralized modal identification, where modal parameters are extracted using a drone-mounted camera. Video is recorded in multiple flight sections to capture civil structures in high resolution and is calibrated to correct lens distortion. Fiducial markers on the structure serve as virtual sensors for identifying marker and corner pixel coordinates. Homography transformation is used to determine marker position and orientation in real-world coordinates, followed by egomotion compensation to obtain absolute displacement and acceleration along with modal identification. The proposed method is validated through both laboratory and field experiments, demonstrating its effectiveness in structural modal identification.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".