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Authors: Tashiro Kyosuke ; Takeda Koji ; Aoki Shogo ; Ye Haoming ; Hiroki Tomoe and Tanaka Kanji

Affiliation: University of Fukui, 3-9-1, Bunkyo, Fukui, Fukui, Japan

Keyword(s): Network Architecture Search, Visual Burr Detection, High-mix Low-volume Production, Non-convex Cylindrical Metal Objects.

Abstract: Visual defect detection (VDD) for high-mix low-volume production of non-convex metal objects, such as high-pressure cylindrical piping joint parts (VDD-HPPPs), is challenging because subtle difference in domain (e.g., metal objects, imaging device, viewpoints, lighting) significantly affects the specular reflection characteristics of individual metal object types. In this paper, we address this issue by introducing a tailor-made VDD framework that can be automatically adapted to a new domain. Specifically, we formulate this adaptation task as the problem of network architecture search (NAS) on a deep object-detection network, in which the network architecture is searched via reinforcement learning. We demonstrate the effectiveness of the proposed framework using the VDD-HPPPs task as a factory case study. Experimental results show that the proposed method achieved higher burr detection accuracy compared with the baseline method for data with different training/test domains for the no n-convex HPPPs, which are particularly affected by domain shifts. (More)

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Paper citation in several formats:
Kyosuke, T.; Koji, T.; Shogo, A.; Haoming, Y.; Tomoe, H. and Kanji, T. (2022). TaylorMade Visual Burr Detection for High-mix Low-volume Production of Non-convex Cylindrical Metal Objects. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-549-4; ISSN 2184-4313, SciTePress, pages 395-400. DOI: 10.5220/0010816000003122

@conference{icpram22,
author={Tashiro Kyosuke. and Takeda Koji. and Aoki Shogo. and Ye Haoming. and Hiroki Tomoe. and Tanaka Kanji.},
title={TaylorMade Visual Burr Detection for High-mix Low-volume Production of Non-convex Cylindrical Metal Objects},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2022},
pages={395-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010816000003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - TaylorMade Visual Burr Detection for High-mix Low-volume Production of Non-convex Cylindrical Metal Objects
SN - 978-989-758-549-4
IS - 2184-4313
AU - Kyosuke, T.
AU - Koji, T.
AU - Shogo, A.
AU - Haoming, Y.
AU - Tomoe, H.
AU - Kanji, T.
PY - 2022
SP - 395
EP - 400
DO - 10.5220/0010816000003122
PB - SciTePress