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Classification of Similar Objects of Different Sizes Using a Reference Object by Means of Convolutional Neural Networks

: Lehr, Jan; Schlüter, Marian; Krüger, Jörg


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019. Proceedings : Zaragoza, Spain, 10 - 13 September 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-0303-7
ISBN: 978-1-7281-0302-0
ISBN: 978-1-7281-0304-4
International Conference on Emerging Technologies and Factory Automation (ETFA) <24, 2019, Zaragoza>
Conference Paper
Fraunhofer IPK ()

Part identification is relevant in many industrial applications, either for direct recognition of components or assemblies, either as a fully automated process or as an assistance system. Convolutional Neural Networks (CNNs) have proven their worth in image processing, especially in classification tasks. It therefore makes sense to use them for industrial applications. There are major problems with parts that look very similar and can only be identified by their size. In this paper we have considered a subset of screws that all conform to the same norm but are of different sizes. The implicit learning of the screw size is only possible if the images are taken in a fixed distance setup and larger screws are shown larger on the images. In this paper we show that CNNs are able to implicitly measure target objects with the help of reference objects and thus to integrate the object size into the learning process.