Authors:
Janis Mohr
1
;
Finn Breidenbach
2
and
Jörg Frochte
1
Affiliations:
1
Interdisciplinary Institute for Applied Artificial Intelligence and Data Science Ruhr, Bochum University of Applied Science, 42579 Heiligenhaus, Germany
;
2
Trimet Aluminium SE, Aluminiumallee 1, 45356 Essen, Germany
Keyword(s):
Machine Learning, One-shot Identification, Image Recognition, Data Augmentation, Convolutional Neural Networks.
Abstract:
In order to optimise products and comprehend product defects, the production process must be traceable. Machine learning techniques are a modern approach, which can be used to recognise a product in every production step. The goal is a tool with the capability to specifically assign changes in a process step to an individual product or batch. In general, a machine learning system based on a Convolutional Neural Network (CNN) forms a vision subsystem to recognise individual products and return their designation. In this paper an approach to identify objects, which have only been seen once, is proposed. The proposed approach is for applications in production comparable with existing solutions based on siamese networks regarding the accuracy. Furthermore, it is a lightweight architecture with some advantages regarding computation coast in the online prediction use case of some industrial applications. It is shown that together with the described workflow and data augmentation the method
is capable to solve an existing industrial application.
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