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Deep Learning for Semantic Segmentation on Minimal Hardware

  • Sander G. van DijkEmail author
  • Marcus M. Scheunemann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11374)

Abstract

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.

Keywords

Deep learning Semantic segmentation Mobile robotics Computer vision Minimal hardware 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of HertfordshireHertfordshireUK

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