Defect Detection of Micro-Precision Glass Insulated Terminals
- 10.2991/jrnal.k.210521.005How to use a DOI?
- Micro-precision glass insulated terminal; improved Faster R-CNN; missing block detection
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%.
- © 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 - 2021/05/28 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 - 10.2991/jrnal.k.210521.005 ID - Liu2021 ER -