Development of a Computer Vision Framework for Improved Remotely Piloted Aircraft Operations
Why this work is in the frame
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Bibliographic record
Abstract
This thesis proposes a computer vision framework to enable improved operations of Remotely Piloted Aircraft equipped with onboard image sensors. The main use of payload image sensors is to provide visual imagery data to the system for real-time or postprocessing applications; the application of an image quality metric and the ground sampling distance of the image sensor can be used to predict the performance of an image sensor in enabling the image classification task. This information is used to determine the mission-specific operational envelope of the aircraft, to ensure that visual data quality requirements are met. The application of a convolutional neural network for image processing is also presented. Finally, a vision-based positioning system is developed; it achieves an average position estimation difference of 18 cm compared to a commercially available indoor localization system and provides a position update rate at 12 Hz.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it