# EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following

arXiv: https://arxiv.org/abs/2106.15045 · HTML: https://mlsanity.com/paper/2106.15045/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Event-Based Motion Segmentation by Motion Compensation](https://mlsanity.com/paper/1904.01293/) (2019)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras](https://mlsanity.com/paper/1906.02919/) (2019)
- [AlphaPilot: Autonomous Drone Racing](https://mlsanity.com/paper/2005.12813/) (2020)
- [GapFlyt: Active Vision Based Minimalist Structure-less Gap Detection For Quadrotor Flight](https://mlsanity.com/paper/1802.05330/) (2018)
- [MorphEyes: Variable Baseline Stereo For Quadrotor Navigation](https://mlsanity.com/paper/2011.03077/) (2020)
- [An Efficient B-spline-Based Kinodynamic Replanning Framework for Quadrotors](https://mlsanity.com/paper/1906.09785/) (2019)
- [Flying batteries: In-flight battery switching to increase multirotor flight time](https://mlsanity.com/paper/1909.10091/) (2019)
- [PRGFlow: Benchmarking SWAP-Aware Unified Deep Visual Inertial Odometry](https://mlsanity.com/paper/2006.06753/) (2020)
- [AprilTags 3D: Dynamic Fiducial Markers for Robust Pose Estimation in Highly Reflective Environments and Indirect Communication in Swarm Robotics](https://mlsanity.com/paper/2001.08622/) (2020)
- [Vision-based Drone Flocking in Outdoor Environments](https://mlsanity.com/paper/2012.01245/) (2020)

## Built on by (most cited first, 17 of 17)

- [DiffPoseNet: Direct Differentiable Camera Pose Estimation](https://mlsanity.com/paper/2203.11174/) (2022)
- [OysterNet: Enhanced Oyster Detection Using Simulation](https://mlsanity.com/paper/2209.08176/) (2022)
- [EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects](https://mlsanity.com/paper/2506.04048/) (2025)
- [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [WorldGen: A Large Scale Generative Simulator](https://mlsanity.com/paper/2210.00715/) (2022)
- [Detecting Olives with Synthetic or Real Data? Olive the Above](https://mlsanity.com/paper/2308.08271/) (2023)
- [EvDetMAV: Generalized MAV Detection from Moving Event Cameras](https://mlsanity.com/paper/2506.19416/) (2025)
- [Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire](https://mlsanity.com/paper/2509.12997/) (2025)
- [Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering](https://mlsanity.com/paper/2603.01997/) (2026)
- [Real-Time Drone Detection in Event Cameras via Per-Pixel Frequency Analysis](https://mlsanity.com/paper/2603.08386/) (2026)
- [HelixTrack: Event-Based Tracking and RPM Estimation of Propeller-like Objects](https://mlsanity.com/paper/2603.09235/) (2026)
- [ASUMOT: Motion-Consistency-Based Asynchronous UAV Detection and Tracking with Event Cameras](https://mlsanity.com/paper/2607.11303/) (2026)
- [ODD-SEC: Onboard Drone Detection with a Spinning Event Camera](https://mlsanity.com/paper/2603.06265/) (2026)
- [Relative State Estimation using Event-Based Propeller Sensing](https://mlsanity.com/paper/2604.18289/) (2026)
- [MinNav: Minimalist Navigation Using Optical Flow For Active Tiny Aerial Robots](https://mlsanity.com/paper/2606.07813/) (2026)
- [EventRadar: Long-Range Visual UAV Discovery through Spatiotemporal Event Sensing](https://mlsanity.com/paper/2606.11285/) (2026)

## Abstract (arXiv)

The rapid rise of accessibility of unmanned aerial vehicles or drones pose a threat to general security and confidentiality. Most of the commercially available or custom-built drones are multi-rotors and are comprised of multiple propellers. Since these propellers rotate at a high-speed, they are generally the fastest moving parts of an image and cannot be directly "seen" by a classical camera without severe motion blur. We utilize a class of sensors that are particularly suitable for such scenarios called event cameras, which have a high temporal resolution, low-latency, and high dynamic range. In this paper, we model the geometry of a propeller and use it to generate simulated events which are used to train a deep neural network called EVPropNet to detect propellers from the data of an event camera. EVPropNet directly transfers to the real world without any fine-tuning or retraining. We present two applications of our network: (a) tracking and following an unmarked drone and (b) landing on a near-hover drone. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with different propeller shapes and sizes. Our network can detect propellers at a rate of 85.1% even when 60% of the propeller is occluded and can run at upto 35Hz on a 2W power budget. To our knowledge, this is the first deep learning-based solution for detecting propellers (to detect drones). Finally, our applications also show an impressive success rate of 92% and 90% for the tracking and landing tasks respectively.