Drone segmentation and orientation detection using a SPAD array camera

Stirling Scholes, Alice Ruget, Germán Mora Martín, Feng Zhu, Istvan Gyongy, Robert K. Henderson, Jonathan Leach*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

The recent development of single-photon avalanche diode (SPADs) arrays as imaging sensors with both picosecond binning capabilities and single photon sensitivity has led to the rapid development of time-of-flight imaging systems. When used in conjunction with a synchronised light source these sensors produce a 3D image. Here, we apply this 3D imaging ability to the problem of drone identification, orientation, and, segmentation. The proliferation of semi-autonomous aerial multi-copters i.e. drones, has raised concerns over the ability of existing aerial detection systems to accurately characterise such vehicles. Here, we fuse the 3D imaging of SPAD sensors with the classification capabilities of a bespoke convolutional neural network (CNN) into a system capable of determining drone pose in flight. To overcome the lack of publicly available training data we generate a photo-realistic dataset to enable the training of our network. After training, we are able to predict the roll, pitch, and yaw of the several different drone types with an accuracy greater than 90%.

Original languageEnglish
Title of host publicationAdvanced Photon Counting Techniques XVI
EditorsMark A. Itzler, Joshua C. Bienfang, K. Alex McIntosh
PublisherSPIE
ISBN (Electronic)9781510650558
ISBN (Print)9781510650541
DOIs
Publication statusPublished - 30 May 2022
EventSPIE Defense + Commercial Sensing 2022 - Virtual, Online
Duration: 6 Jun 202212 Jun 2022

Publication series

NameProceedings of SPIE
Volume12089
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSPIE Defense + Commercial Sensing 2022
CityVirtual, Online
Period6/06/2212/06/22

Keywords

  • CNN
  • Drone pose
  • Lidar
  • SPAD

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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