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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.

Fulltext ()

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

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.