# UAV Detection: A STDP trained Deep Convolutional Spiking Neural Network Retina-Neuromorphic Approach

arXiv: https://arxiv.org/abs/1904.12230 · HTML: https://mlsanity.com/paper/1904.12230/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025)
- [Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire](https://mlsanity.com/paper/2509.12997/) (2025)
- [Unsupervised Spiking Instance Segmentation on Event Data using STDP](https://mlsanity.com/paper/2111.05283/) (2021)

## Abstract (arXiv)

The Dynamic Vision Sensor (DVS) has many attributes that allow it to be well suited to the task for UAV Detection. This paper is the first to look at exploiting the features of an Event Camera solely for Drone Detection while combining it with a Spiking Neural Network (SNN) trained using the unsupervised approach of Spike-Time-Dependent Plasticity (STDP) for feature and pattern recognition for detection. Highlighting the key features and current drawbacks with the technology while comparing real and simulated data to show how future devices could overcome these drawbacks to help tackle this current problem.