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dc.contributor.authorDijk, Sander G. van
dc.contributor.authorScheunemann, Marcus M.
dc.contributor.editorHolz, Dirk
dc.contributor.editorGenter, Katie
dc.contributor.editorSaad, Maarouf
dc.contributor.editorvon Stryk, Oskar
dc.date.accessioned2019-09-25T00:02:58Z
dc.date.available2019-09-25T00:02:58Z
dc.date.issued2019-08-04
dc.identifier.citationDijk , S G V & Scheunemann , M M 2019 , Deep Learning for Semantic Segmentation on Minimal Hardware . in D Holz , K Genter , M Saad & O von Stryk (eds) , RoboCup 2018 : Robot World Cup XXII . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11374 LNAI , Springer Verlag , pp. 349-361 , RoboCup 2018 Symposium , Montreal , Canada , 22/06/18 . https://doi.org/10.1007/978-3-030-27544-0_29
dc.identifier.citationconference
dc.identifier.isbn9783030275433
dc.identifier.isbn9783030275440
dc.identifier.issn0302-9743
dc.identifier.otherPURE: 17414832
dc.identifier.otherPURE UUID: b7197f40-dc8e-4d9c-b4ca-c3d5993403cc
dc.identifier.otherArXiv: http://arxiv.org/abs/1807.05597v1
dc.identifier.otherScopus: 85070709697
dc.identifier.otherORCID: /0000-0002-0815-7024/work/62752105
dc.identifier.urihttp://hdl.handle.net/2299/21699
dc.description© Springer Nature Switzerland AG 2019
dc.description.abstractDeep 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.en
dc.format.extent13
dc.language.isoeng
dc.publisherSpringer Verlag
dc.relation.ispartofRoboCup 2018
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsEmbargoed
dc.subjectRobotics
dc.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectComputer Vision
dc.subjectSemantic Segmentation
dc.subjectMinimal Hardware
dc.subjectMobile Robotics
dc.subjectDeep learning
dc.subjectComputer vision
dc.subjectMinimal hardware
dc.subjectSemantic segmentation
dc.subjectMobile robotics
dc.subjectArtificial Intelligence
dc.subjectSignal Processing
dc.subjectComputer Science(all)
dc.subjectTheoretical Computer Science
dc.titleDeep Learning for Semantic Segmentation on Minimal Hardwareen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionAdaptive Systems
dc.date.embargoedUntil2020-08-04
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85070709697&partnerID=8YFLogxK
dc.relation.schoolSchool of Computer Science
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2019-08-04
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1007/978-3-030-27544-0_29
rioxxterms.licenseref.uriOther
rioxxterms.licenseref.startdate2020-08-04
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.date.embargo2020-08-04
herts.rights.accesstypeEmbargoed


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