Influence of propeller downwash on drone-based active infrared thermographic inspection
Bibliographic record
Abstract
Drone-based inspection systems offer a fast and effective approach that can significantly reduce inspection cost and time. Infrared cameras mounted on drones have been used in several surveillance and monitoring applications. However, for high-resolution inspection that requires the drone to be in close proximity to structures, the propeller’s convection effect can impact the structure’s temperature being monitored. This could be particularly important during an active infrared thermography inspection, when the drone has to hover for a prolonged duration to monitor the temperature evolution of the parts being inspected. This paper presents the results of experiments performed to assess the effect of propeller downwash on drone-based active infrared thermographic inspection. The results are presented for a mockup panel with impact damage and simulated delamination defects. It was found that the drone propellers had a significant effect on the temperature evolution pattern by reducing the peak temperature reached during the heating phase of the inspection, as well as accelerating the cooling rate. Nonetheless, the damage detection capability seems unaffected with only small variations in the contrast of the processed images.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".