An Approach to One-shot Identification with Neural Networks

Janis Mohr, Finn Breidenbach, Jörg Frochte

2021

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.

Paper Citation

in Harvard Style

Mohr J., Breidenbach F. and Frochte J. (2021). An Approach to One-shot Identification with Neural Networks. In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA; ISBN 978-989-758-534-0, SciTePress, pages 344-351. DOI: 10.5220/0010684300003063

in Bibtex Style

@conference{ncta21,
author={Janis Mohr and Finn Breidenbach and Jörg Frochte},
title={An Approach to One-shot Identification with Neural Networks},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA},
year={2021},
pages={344-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010684300003063},
isbn={978-989-758-534-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA
TI - An Approach to One-shot Identification with Neural Networks
SN - 978-989-758-534-0
AU - Mohr J.
AU - Breidenbach F.
AU - Frochte J.
PY - 2021
SP - 344
EP - 351
DO - 10.5220/0010684300003063
PB - SciTePress