# Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire

arXiv: https://arxiv.org/abs/2509.12997 · HTML: https://mlsanity.com/paper/2509.12997/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline](https://mlsanity.com/paper/2304.06793/) (2023)
- [Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware](https://mlsanity.com/paper/1812.01739/) (2018)
- [Optimizing the energy consumption of spiking neural networks for neuromorphic applications](https://mlsanity.com/paper/1912.01268/) (2019)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends](https://mlsanity.com/paper/2008.12461/) (2020)
- [Low-power event-based face detection with asynchronous neuromorphic hardware](https://mlsanity.com/paper/2312.14261/) (2023)
- [UAV Detection: A STDP trained Deep Convolutional Spiking Neural Network Retina-Neuromorphic Approach](https://mlsanity.com/paper/1904.12230/) (2019)

## Built on by (most cited first, 2 of 2)

- [Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering](https://mlsanity.com/paper/2603.01997/) (2026)
- [ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras](https://mlsanity.com/paper/2607.11303/) (2026)

## Abstract (arXiv)

Small drones are an increasing threat to both military personnel and civilian infrastructure, making early and automated detection crucial. In this work we develop a system that uses spiking neural networks and neuromorphic cameras (event cameras) to detect drones. The detection model is deployed on a neuromorphic chip making this a fully neuromorphic system. Multiple detection units can be deployed to create a virtual tripwire which detects when and where drones enter a restricted zone. We show that our neuromorphic solution is several orders of magnitude more energy efficient than a reference solution deployed on an edge GPU, allowing the system to run for over a year on battery power. We investigate how synthetically generated data can be used for training, and show that our model most likely relies on the shape of the drone rather than the temporal characteristics of its propellers. The small size and low power consumption allows easy deployment in contested areas or locations that lack power infrastructure.