Journal of Robotics, Networking and Artificial Life

Volume 8, Issue 1, June 2021, Pages 18 - 23

Defect Detection of Micro-Precision Glass Insulated Terminals

Authors
Qunpo Liu1, *, Mengke Wang1, Zonghui Liu1, Bo Su1, Naohiko Hanajima2
1School of Electrical Engineering and Automation, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo, Henan 454003, P. R. China
2College of Information and Systems, Muroran Institute of Technology, 27-1 Mizumoto-cho, Hokkaido, Muroran-shi, Hokkaido 050-8585, Japan
*Corresponding author. Email: lqpny@hpu.edu.cn
Corresponding Author
Qunpo Liu
Received 15 October 2020, Accepted 28 March 2021, Available Online 28 May 2021.
DOI
https://doi.org/10.2991/jrnal.k.210521.005How to use a DOI?
Keywords
Micro-precision glass insulated terminal, improved Faster R-CNN, missing block detection
Abstract

Micro-precision Glass Insulated Terminals (referred to as glass terminals) are the core components used in precision electronic equipment and are often used for electrical connections between modules. As a glass terminal, its quality has a great influence on the performance of precision electronic equipment. Due to the limitations of materials and production processes, some of the glass terminals produced have defects, such as missing blocks, pores and cracks. At present, most of the defect detection of glass terminals is done by manual inspection, and rapid detection easily causes eye fatigue, so it is difficult to ensure product quality and production efficiency. The traditional defect detection technology is difficult to effectively detect the very different defects of the glass terminal. Therefore, this paper proposes to use deep learning technology to detect missing blocks. First, preprocess the sample pictures of the missing block defects of the glass terminal, and then train the improved Faster Region-CNN deep learning network for defect detection. According to the test results, the accuracy of the algorithm in detecting missing defects in the glass terminal is as high as 93.52%.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
8 - 1
Pages
18 - 23
Publication Date
2021/05
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.k.210521.005How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Qunpo Liu
AU  - Mengke Wang
AU  - Zonghui Liu
AU  - Bo Su
AU  - Naohiko Hanajima
PY  - 2021
DA  - 2021/05
TI  - Defect Detection of Micro-Precision Glass Insulated Terminals
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 18
EP  - 23
VL  - 8
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.210521.005
DO  - https://doi.org/10.2991/jrnal.k.210521.005
ID  - Liu2021
ER  -