Synthetic Dataset for Quadcopter Detection Based on Frequency Propeller Signature
Bibliographic record
Abstract
The use of computer graphic tools typically associated with video games is a popular method to generate synthetic datasets for the training of machine learning algorithms. For optical detection of quadcopters, realistic imagery needs to be generated for multiple models of drones, in multiple types of environments and different flight profiles. By itself, the effort required to generate those virtual environments can be as important as flying actual drones. This is particularly true when a physics-based engine is required to model the behavior of the propellers. While some appearance-based drone detection methods may not need accurate propeller behavior, other detection methods that exploit the high-frequency and/or temporal signatures generated by rotating propellers do require such accurate simulations. This is especially the case for neuromorphic sensors, which are generally sensitive to the unique high-frequency visual signal from the propeller blades. We propose a synthetic approach to acquire training datasets for neuromorphic sensors using a flexible hardware system built from quadcopter components. This system allows efficient acquisition of training sets for drone detection sensors based on the propeller’s temporal and/or frequency signature.
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How this classification was reachedexpand
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 | 0.001 |
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 teacher head, 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".