Vision sensing based intelligent detection of surface defect and its industrial applications

被引：0
|
作者：
Chai L. [1 ]
Ren L. [2 ]
Gu K. [3 ]
Chen J. [4 ]
Huang B. [5 ,6 ]
Ye Q. [1 ]
Cao W. [7 ]
机构：
[1] College of Control Science and Engineering, Zhejiang University, Hangzhou
[2] School of Automation Science and Electrical Engineering, Beihang University
[3] Faculty of Information Technology, Beijing University of Technology, Beijing
[4] School of Computer Science and Engineering, Beihang University, Beijing
[5] College of Mechanical and Electrical Engineering, Northwestern Polytechnical University, Xi'an
[6] Shanghai Municipal Key Laboratory of Aircraft Engine Digital Twin, Shanghai
[7] AECC Commercial Aircraft Engine Co. Ltd., Shanghai
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 07期
基金：
中国国家自然科学基金;
关键词：
artificial intelligence; deep learning; few-shot object detection; image processing; intelligent detection; surface defect detection; vision sensing;
D O I：
10.13196/j.cims.2022.07.006
中图分类号：
学科分类号：
摘要：
Due to the unparalleled advantage at the quality control of industrial products, intelligent detection of surfacedefect based on vision sensing has attracted ever-increasing attention and been applied in a wide range of industries, such as automotive industry, semiconductor manufacturing, glass fabrication, steel metallurgy. The rapid development of AI learning algorithms and the vision sensing technology has brought new opportunity and challenges to the detection of surface defect. A survey of methodologies and trends in the vision based surface detection was summarized, with special focus on modern image processing, geometric deep learning and deep learning methods for object detection, which could prompt a technological breakthrough to the intelligent detection of surface defect. The applications of industrial image detection were discussed by three typical fields including steel metallurgy, air pollution monitoring and defect detection of aircraft engine. Several challenging issues were envisioned for future research. © 2022 CIMS. All rights reserved.
引用
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页码：1996 / 2004
页数：8
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