Drone Virtual Fence Using a Neuromorphic Camera
Why this work is in the frame
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Bibliographic record
Abstract
Neuromorphic cameras are well suited to detect the motion of propellers (blades) on Unmanned Aerial Systems (UAS), or drones. In this paper, we introduce the concept of a virtual fence which is a low-cost networked situational awareness device to quickly alert that a drone has entered the zone. Neuromorphic cameras significantly reduce the amount of data that must be processed as opposed to conventional cameras. Processing is required only when events are generated. Those events can be generated by a drone, by another low altitude airborne object (projectiles or birds), or by variations in the background. We propose two complementary algorithms that allow us to differentiate the signature of propeller blades from other events. Those algorithms exploit the periodic nature of propellers’ signal and the presence of sub-harmonics in the detected signal. Those sub-harmonics are introduced in the signal when a camera pixel misses some high-frequency events. We also show how to adjust the optics of the camera so as to reduce the contrast of background events, simplifying the categorization task. A prototype of a system consuming, during normal operations, 5.14 W with a battery autonomy of to 27 hours is presented. This prototype can detect drones up to an altitude of 9 m using a DAVIS 346 from IniVation with a field of view of about 70 degrees. Based on the actual improvement in resolution of current and next generation neuromorphic cameras, it is expected that the range of detection will increase and the virtual fence concept could be deployed operationally in the next few years.
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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