mlsanityData to 14 Oct 2026

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

arXiv 2112.05488 ↗First posted 10 Dec 2021cs.CVcited by 1

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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.

Key facts. Cited by 1 paper 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

12 of 130 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2017A Deep Learning Approach to Drone Monitoringcited by 16
  5. 2018Drone Detection Using Depth Mapscited by 8
  6. 2018Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networkscited by 8
  7. 2017Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networkscited by 7
  8. 2020Effect of Annotation Errors on Drone Detection with YOLOv3cited by 7
  9. 2020Drone classification from RF fingerprints using deep residual netscited by 7
  10. 2021High-speed object detection with a single-photon time-of-flight image sensorcited by 7
  11. 2019Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniquescited by 6
  12. 2018Rogue Drone Detection: A Machine Learning Approachcited by 2

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Drone Detection using Deep Neural Networks Trained on Pure Synthetic Datacited by 1