Inspection of Industrial Coatings based on Multispectral BTF

Ryosuke Suzuki, Fumihiko Sakaue, Jun Sato, Ryuichi Fukuta, Taketo Harada, Kazuhisa Ishimaru

2021

Abstract

In this paper, we propose a method to inspect coatings of industrial products in a factory automation system. The coating of industrial products is important because the coating directly affects the impression of the product, and a large amount of cost is spent on its inspection. Because lots of colors are used in the coating of industrial products, as well as there are various surface treatments such as matte and mirror finishes, the appearance of these products varies hugely. Therefore, it is difficult to obtain the properties of the surfaces by ordinary camera systems, and thus, they are inspected manually in the current system in most cases. In this paper, we present a method of representing surface properties of them, called multispectral BTF, by taking products under narrow-band light from various directions. We also show a method for inspection using a one-class discriminator based on Deep Neural Network using the multispectral BTF. Several experimental results show that our proposed BTF and one-class classifier can inspect various kinds of coating.

Paper Citation

in Harvard Style

Suzuki R., Sakaue F., Sato J., Fukuta R., Harada T. and Ishimaru K. (2021). Inspection of Industrial Coatings based on Multispectral BTF. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 156-162. DOI: 10.5220/0010268101560162

in Bibtex Style

@conference{visapp21,
author={Ryosuke Suzuki and Fumihiko Sakaue and Jun Sato and Ryuichi Fukuta and Taketo Harada and Kazuhisa Ishimaru},
title={Inspection of Industrial Coatings based on Multispectral BTF},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={156-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010268101560162},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - Inspection of Industrial Coatings based on Multispectral BTF
SN - 978-989-758-488-6
AU - Suzuki R.
AU - Sakaue F.
AU - Sato J.
AU - Fukuta R.
AU - Harada T.
AU - Ishimaru K.
PY - 2021
SP - 156
EP - 162
DO - 10.5220/0010268101560162
PB - SciTePress