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Volume 7, Issue 3, December 2020, Pages 212 - 216
Detection Algorithm of Porosity Defect on Surface of Micro-precision Glass Encapsulated Electrical Connectors
Authors
Qunpo Liu1, *, Manli Wang1, Gaowei Wang1, Ruxin Gao1, Naohiko Hanajima2
1Department of Robotics Engineering, 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 2019, Accepted 13 June 2020, Available Online 16 September 2020.
- DOI
- 10.2991/jrnal.k.200909.015How to use a DOI?
- Keywords
- Micro-precision glass encapsulated electrical connectors; manual inspection; surface defect inspection; feature extraction
- Abstract
A miniature precision glass encapsulated electrical connectors introduced by glass powder and metal wires through a special complicated process. Aiming at the porosity defects on the surface, a defect detection algorithm propose based on threshold segmentation and feature extraction. Pre-operation, global threshold segmentation processing and feature extraction (based on area, circularity aspect ratio, compactness, and contour length) are preformed to detect the defects. Experimental results show that the algorithm can accurately identify porosities defects.
- Copyright
- © 2020 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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TY - JOUR AU - Qunpo Liu AU - Manli Wang AU - Gaowei Wang AU - Ruxin Gao AU - Naohiko Hanajima PY - 2020 DA - 2020/09/16 TI - Detection Algorithm of Porosity Defect on Surface of Micro-precision Glass Encapsulated Electrical Connectors JO - Journal of Robotics, Networking and Artificial Life SP - 212 EP - 216 VL - 7 IS - 3 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.200909.015 DO - 10.2991/jrnal.k.200909.015 ID - Liu2020 ER -