Deep Learning for Semantic Segmentation on Minimal Hardware

Sander G. van Dijk, Marcus M. Scheunemann

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

3 Citations (Scopus)
15 Downloads (Pure)


Deep learning has revolutionised many fields, but it is still challenging to transfer its success to small mobile robots with minimal hardware. Specifically, some work has been done to this effect in the RoboCup humanoid football domain, but results that are performant and efficient and still generally applicable outside of this domain are lacking. We propose an approach conceptually different from those taken previously. It is based on semantic segmentation and does achieve these desired properties. In detail, it is being able to process full VGA images in real-time on a low-power mobile processor. It can further handle multiple image dimensions without retraining, it does not require specific domain knowledge to achieve a high frame rate and it is applicable on a minimal mobile hardware.
Original languageEnglish
Title of host publicationRoboCup 2018
Subtitle of host publicationRobot World Cup XXII
EditorsDirk Holz, Katie Genter, Maarouf Saad, Oskar von Stryk
PublisherSpringer Nature
Number of pages13
ISBN (Electronic)9783030275440
ISBN (Print)9783030275433
Publication statusPublished - 4 Aug 2019
EventRoboCup 2018 Symposium - Palais des congrès, Montreal, Canada
Duration: 22 Jun 201822 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11374 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceRoboCup 2018 Symposium
Internet address


  • Robotics
  • Machine Learning
  • Deep Learning
  • Computer Vision
  • Semantic Segmentation
  • Minimal Hardware
  • Mobile Robotics
  • Deep learning
  • Computer vision
  • Minimal hardware
  • Semantic segmentation
  • Mobile robotics


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