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        Deep Learning for Semantic Segmentation on Minimal Hardware

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        2018_Deep_Learning_for_Semantic_Segmentation_on_Minimal_Hardware_1.pdf (PDF, 3Mb)
        Author
        Dijk, Sander G. van
        Scheunemann, Marcus M.
        Attention
        2299/21699
        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.
        Publication date
        2019-08-04
        Published in
        RoboCup 2018
        Published version
        https://doi.org/10.1007/978-3-030-27544-0_29
        Other links
        http://hdl.handle.net/2299/21699
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