research product . 2019

Deep Learning for Semantic Segmentation on Minimal Hardware

Dijk, Sander G. van; Scheunemann, Marcus M.;
  • Published: 04 Aug 2019
  • Publisher: Springer Verlag
  • Country: United Kingdom
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.
© Springer Nature Switzerland AG 2019
free text keywords: Robotics, Machine Learning, Deep Learning, Computer Vision, Semantic Segmentation, Minimal Hardware, Mobile Robotics, Artificial Intelligence, Signal Processing, Computer Science(all), Theoretical Computer Science
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