# Drone Detection with Event Cameras

arXiv: https://arxiv.org/abs/2508.04564 · HTML: https://mlsanity.com/paper/2508.04564/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 80 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 30 of 40)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [Video to Events: Recycling Video Datasets for Event Cameras](https://mlsanity.com/paper/1912.03095/) (2019)
- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline](https://mlsanity.com/paper/2309.14611/) (2023)
- [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)
- [Temporal Binary Representation for Event-Based Action Recognition](https://mlsanity.com/paper/2010.08946/) (2020)
- [Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset](https://mlsanity.com/paper/2412.06647/) (2024)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras](https://mlsanity.com/paper/2401.02826/) (2024)
- [FRED: The Florence RGB-Event Drone Dataset](https://mlsanity.com/paper/2506.05163/) (2025)
- [Neuromorphic Drone Detection: an Event-RGB Multimodal Approach](https://mlsanity.com/paper/2409.16099/) (2024)
- [Monocular Event-Based Vision for Obstacle Avoidance with a Quadrotor](https://mlsanity.com/paper/2411.03303/) (2024)
- [Event-based Tiny Object Detection: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2506.23575/) (2025)
- [Flying Objects Detection from a Single Moving Camera](https://mlsanity.com/paper/1411.7715/) (2014)
- [Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors](https://mlsanity.com/paper/2403.11875/) (2024)

## Built on by (most cited first, 9 of 9)

- [Real-Time Drone Detection in Event Cameras via Per-Pixel Frequency Analysis](https://mlsanity.com/paper/2603.08386/) (2026)
- [ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras](https://mlsanity.com/paper/2607.11303/) (2026)
- [Relative State Estimation using Event-Based Propeller Sensing](https://mlsanity.com/paper/2604.18289/) (2026)
- [Frequency-domain Event-based Imaging for Selective Surveillance](https://mlsanity.com/paper/2605.15392/) (2026)
- [See Silhouettes in Motion with Neuromorphic Vision](https://mlsanity.com/paper/2605.17984/) (2026)
- [Programmable Silicon Retina on Pixel Processor Array](https://mlsanity.com/paper/2606.08370/) (2026)
- [SkyEV: RGB-Event UAV detection and tracking dataset and baseline](https://mlsanity.com/paper/2607.18747/) (2026)
- [Beyond Symmetric Fusion: Exploiting Task-Dependent Modality Strengths for RGB-Event Small Object Detection](https://mlsanity.com/paper/2608.01302/) (2026)
- [Residual Kalman Dynamics for Event-Based UAV Forecasting](https://mlsanity.com/paper/2609.00839/) (2026)

## Abstract (arXiv)

The diffusion of drones presents significant security and safety challenges. Traditional surveillance systems, particularly conventional frame-based cameras, struggle to reliably detect these targets due to their small size, high agility, and the resulting motion blur and poor performance in challenging lighting conditions. This paper surveys the emerging field of event-based vision as a robust solution to these problems. Event cameras virtually eliminate motion blur and enable consistent detection in extreme lighting. Their sparse, asynchronous output suppresses static backgrounds, enabling low-latency focus on motion cues. We review the state-of-the-art in event-based drone detection, from data representation methods to advanced processing pipelines using spiking neural networks. The discussion extends beyond simple detection to cover more sophisticated tasks such as real-time tracking, trajectory forecasting, and unique identification through propeller signature analysis. By examining current methodologies, available datasets, and the distinct advantages of the technology, this work demonstrates that event-based vision provides a powerful foundation for the next generation of reliable, low-latency, and efficient counter-UAV systems.