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Assessing the performance of Neural Networks in Recognizing Manual Labor Actions in a Production Environment

dc.contributor.authorHöfinghoff, Maximilian
dc.contributor.authorBuschermöhle, Ralf
dc.contributor.authorKorn, Goy-Hinrich
dc.contributor.authorSchumacher, Marcel
dc.contributor.authorSeipolt, Arne
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorWohlgemuth, Volker
dc.date.accessioned2023-11-29T14:50:20Z
dc.date.available2023-11-29T14:50:20Z
dc.date.issued2023
dc.description.abstractAction recognition technology has gained significant traction in recent years. This paper focuses on evaluating neural network architectures for action recognition in the production industry. By utilizing datasets tailored for production or assembly tasks, various architectures are assessed for their accuracy and performance. The findings of this study provide some insights and guidance for researchers and practitioners to select an appropriate architecture or pretrained models for action recognition in the production industry.en
dc.identifier.doi10.18420/inf2023_148
dc.identifier.isbn978-3-88579-731-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43072
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2023 - Designing Futures: Zukünfte gestalten
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-337
dc.subjectAction Recognition
dc.subjectProduction
dc.subjectBenchmark
dc.subjectMachine Learning
dc.titleAssessing the performance of Neural Networks in Recognizing Manual Labor Actions in a Production Environmenten
dc.typeText/Conference Paper
gi.citation.endPage1433
gi.citation.publisherPlaceBonn
gi.citation.startPage1421
gi.conference.date26.-29. September 2023
gi.conference.locationBerlin
gi.conference.sessiontitleÖkologische Nachhaltigkeit - Zukunft nachhaltig gestalten durch digitalisierte Wertschöpfungsprozesse (DigiWe)

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