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Towards Deep Learning in Industrial Applications Taking Advantage of Service-Oriented Architectures

 
: Briese, C.; Schlüter, M.; Lehr, J.; Maurer, K.; Krüger, J.

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Fulltext ()

Procedia manufacturing 43 (2020), pp.503-510
ISSN: 2351-9789
Global Conference on Sustainable Manufacturing (GCSM) <17, 2019, Shanghai>
English
Journal Article, Conference Paper, Electronic Publication
Fraunhofer IPK ()

Abstract
In reverse logistics, identification of products is necessary but due to uninterpretable markers information flow is not always consistent. Recent image-based recognition developments using Convolutional Neural Networks are promising but collecting required labeled data is time- and cost-intensive. To allow a quick deployment and usage of such systems, we present a conceptual service-oriented architecture that enables Deep Learning recognition systems to be used with initially small but growing data sets, as with every usage training data expands on run-time. An identification problem is reduced to digitization and labeling of data and as a side effect digital knowledge retention can be established in companies.

: http://publica.fraunhofer.de/documents/N-602636.html