# DronePose: The identification, segmentation, and orientation detection of drones via neural networks

arXiv: https://arxiv.org/abs/2112.05488 · HTML: https://mlsanity.com/paper/2112.05488/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 130 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A Deep Learning Approach to Drone Monitoring](https://mlsanity.com/paper/1712.00863/) (2017)
- [Drone Detection Using Depth Maps](https://mlsanity.com/paper/1808.00259/) (2018)
- [Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks](https://mlsanity.com/paper/1812.08333/) (2018)
- [Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks](https://mlsanity.com/paper/1701.05779/) (2017)
- [Effect of Annotation Errors on Drone Detection with YOLOv3](https://mlsanity.com/paper/2004.01059/) (2020)
- [Drone classification from RF fingerprints using deep residual nets](https://mlsanity.com/paper/2011.13663/) (2020)
- [High-speed object detection with a single-photon time-of-flight image sensor](https://mlsanity.com/paper/2107.13407/) (2021)
- [Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques](https://mlsanity.com/paper/1901.07703/) (2019)
- [Rogue Drone Detection: A Machine Learning Approach](https://mlsanity.com/paper/1805.05138/) (2018)

## Built on by (most cited first, 1 of 1)

- [Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data](https://mlsanity.com/paper/2411.09077/) (2024)

## Abstract (arXiv)

The growing ubiquity of drones has raised concerns over the ability of traditional air-space monitoring technologies to accurately characterise such vehicles. Here, we present a CNN using a decision tree and ensemble structure to fully characterise drones in flight. Our system determines the drone type, orientation (in terms of pitch, roll, and yaw), and performs segmentation to classify different body parts (engines, body, and camera). We also provide a computer model for the rapid generation of large quantities of accurately labelled photo-realistic training data and demonstrate that this data is of sufficient fidelity to allow the system to accurately characterise real drones in flight. Our network will provide a valuable tool in the image processing chain where it may build upon existing drone detection technologies to provide complete drone characterisation over wide areas.