Embedded vision system for automated drone landing site detection

Patryk Fraczek, Andre Mora, Tomasz Kryjak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)


This paper presents an embedded video subsystem used to classify the terrain, based on an image from a camera located under the drone, for the purpose of an automatic landing system. Colour and texture features, as well as decision trees and support vector machine classifiers were analysed and evaluated. The algorithm was supported with a shadow detection module. It was evaluated on 100 test cases and achieved over 80% performance. The designed video system was implemented on two embedded platforms – a Zynq SoC (System on Chip – Field Programmable Gate Array + ARM processor system) and a Jetson GPU (Graphic Processing Unit + ARM processor system). The performance achieved on both architectures is compared and discussed.

Original languageEnglish
Title of host publicationComputer Vision and Graphics - International Conference, ICCVG 2018, 2018, Proceedings
EditorsL. Chmielewski, R. Kozera, A. Orłowski, K. Wojciechowski, A. Bruckstein, N. Petkov
Place of PublicationCham
PublisherSpringer Verlag
Number of pages13
ISBN (Electronic)978-3-030-00692-1
ISBN (Print)978-3-030-00691-4
Publication statusPublished - 2018
EventInternational Conference on Computer Vision and Graphics, ICCVG 2018 - Warsaw, Poland
Duration: 17 Sept 201819 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Volume11114 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Computer Vision and Graphics, ICCVG 2018


  • Decision Trees (DT)
  • Digital image processing
  • FPGA
  • GPU
  • Machine learning
  • Safe landing site detection
  • Support Vector Machine (SVM)
  • Unmanned Aerial Vehicle (UAV)
  • Zynq


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