Deep-Learning Based Segmentation Algorithm for Defect Detection in Magnetic Particle Testing Images *
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- Ueda Akira
- Department of Mechanical and Control Engineering, Kyushu Institute of Technology
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- Lu Huimin
- Department of Mechanical and Control Engineering, Kyushu Institute of Technology
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- Kamiya Tohru
- Department of Mechanical and Control Engineering, Kyushu Institute of Technology
説明
Magnetic Particle Testing (MPT), also referred to as magnetic particle inspection, is a nondestructive examination (NDE) technique used to detect surface and slightly subsurface flaws in most ferromagnetic materials such as iron, nickel, and cobalt, and some of their alloys. In a bad environment, the procedure is complicated, and automation of MPT is strongly desired. To find defects in the formed magnetic powder pattern, it is required to be highly skilled and automation has been considered difficult. In recent years, many defect detection methods based on deep learning have been proposed, and the effectiveness of deep learning has been shown in the task of automatically detecting various types of defects having different shapes and sizes. In this paper, we describe the development of deep learning based segmentation algorithm for defect detection in MPT images. We have achieved a F2 score of 84.04% by using U-Net as the segmentation model and by utilizing a strong backbone network and an optimal loss function.
収録刊行物
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- 人工生命とロボットに関する国際会議予稿集
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人工生命とロボットに関する国際会議予稿集 26 235-238, 2021-01-21
株式会社ALife Robotics
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詳細情報 詳細情報について
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- CRID
- 1390569700725598208
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- ISSN
- 21887829
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- OpenAIRE
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- 抄録ライセンスフラグ
- 使用不可
