A Survey of Defect Detection Applications Based on Generative Adversarial Networks
With the development of science and technology and the progress of the times, automation and intelligence have been popularized in manufacturing in all walks of life. With the progress of productivity, product defect detection has become an indispensable part. However, in practical scenarios, the ap...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9930483/ |
| _version_ | 1811334877869506560 |
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| author | Xiangjie He Zhengwei Chang Linghao Zhang Houdong Xu Hongbo Chen Zhongqiang Luo |
| author_facet | Xiangjie He Zhengwei Chang Linghao Zhang Houdong Xu Hongbo Chen Zhongqiang Luo |
| author_sort | Xiangjie He |
| collection | DOAJ |
| description | With the development of science and technology and the progress of the times, automation and intelligence have been popularized in manufacturing in all walks of life. With the progress of productivity, product defect detection has become an indispensable part. However, in practical scenarios, the application of supervised deep learning algorithms in the field of defect detection is limited due to the difficulty and unpredictability of obtaining defect samples. In recent years, semi-supervised and unsupervised deep learning algorithms have attracted more and more attention in various defect detection tasks. Generative adversarial networks (GAN), as an unsupervised learning algorithm, has been widely used in defect detection tasks in various fields due to its powerful generation ability. In order to provide some inspiration for the researchers who intend to use GAN for defect detection research. In this paper, the theoretical basis, technical development and practical application of GAN based defect detection are reviewed. This paper also discusses the current outstanding problems of GAN and GAN-based defect detection, and makes a detailed prediction and analysis of the possible future research directions. This paper summarizes the relevant literature on the research progress and application status of GAN based defect detection, which provides certain technical information for researchers who are interested in researching GAN and hope to apply it to defect detection tasks. |
| first_indexed | 2024-04-13T17:15:46Z |
| format | Article |
| id | doaj.art-66bb3fdcd77e4bc3949d42d5261ff83d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T17:15:46Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-66bb3fdcd77e4bc3949d42d5261ff83d2022-12-22T02:38:08ZengIEEEIEEE Access2169-35362022-01-011011349311351210.1109/ACCESS.2022.32172279930483A Survey of Defect Detection Applications Based on Generative Adversarial NetworksXiangjie He0https://orcid.org/0000-0002-0255-0116Zhengwei Chang1Linghao Zhang2Houdong Xu3Hongbo Chen4Zhongqiang Luo5https://orcid.org/0000-0003-1767-1831School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu, ChinaState Grid Sichuan Electric Power Company, Chengdu, ChinaSichuan Shuneng Electric Power Company Ltd., Chengdu, ChinaSchool of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, ChinaWith the development of science and technology and the progress of the times, automation and intelligence have been popularized in manufacturing in all walks of life. With the progress of productivity, product defect detection has become an indispensable part. However, in practical scenarios, the application of supervised deep learning algorithms in the field of defect detection is limited due to the difficulty and unpredictability of obtaining defect samples. In recent years, semi-supervised and unsupervised deep learning algorithms have attracted more and more attention in various defect detection tasks. Generative adversarial networks (GAN), as an unsupervised learning algorithm, has been widely used in defect detection tasks in various fields due to its powerful generation ability. In order to provide some inspiration for the researchers who intend to use GAN for defect detection research. In this paper, the theoretical basis, technical development and practical application of GAN based defect detection are reviewed. This paper also discusses the current outstanding problems of GAN and GAN-based defect detection, and makes a detailed prediction and analysis of the possible future research directions. This paper summarizes the relevant literature on the research progress and application status of GAN based defect detection, which provides certain technical information for researchers who are interested in researching GAN and hope to apply it to defect detection tasks.https://ieeexplore.ieee.org/document/9930483/Deep learninggenerating adversarial networksdefect detectionadversarial learning |
| spellingShingle | Xiangjie He Zhengwei Chang Linghao Zhang Houdong Xu Hongbo Chen Zhongqiang Luo A Survey of Defect Detection Applications Based on Generative Adversarial Networks IEEE Access Deep learning generating adversarial networks defect detection adversarial learning |
| title | A Survey of Defect Detection Applications Based on Generative Adversarial Networks |
| title_full | A Survey of Defect Detection Applications Based on Generative Adversarial Networks |
| title_fullStr | A Survey of Defect Detection Applications Based on Generative Adversarial Networks |
| title_full_unstemmed | A Survey of Defect Detection Applications Based on Generative Adversarial Networks |
| title_short | A Survey of Defect Detection Applications Based on Generative Adversarial Networks |
| title_sort | survey of defect detection applications based on generative adversarial networks |
| topic | Deep learning generating adversarial networks defect detection adversarial learning |
| url | https://ieeexplore.ieee.org/document/9930483/ |
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