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Cited by 200Industrial Vision Systems and Defect DetectionGenerative Adversarial Networks and Image SynthesisInfrastructure Maintenance and Monitoring

The DOI matches its Crossref record; 77 of the 80 references it cites were found in a registry or an index.

Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection

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Zhang, Gongjie, Cui, Kaiwen, Hung, Tzu-Yi, Lu, Shijian·2021·2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
DOI 10.1109/wacv48630.2021.00257
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Abstract

Automated defect inspection is critical for effective and efficient maintenance, repair, and operations in advanced manufacturing. On the other hand, automated defect inspection is often constrained by the lack of defect samples, especially when we adopt deep neural networks for this task. This paper presents Defect-GAN, an automated defect synthesis network that generates realistic and diverse defect samples for training accurate and robust defect inspection networks. Defect-GAN learns through defacement and restoration processes, where the defacement generates defects on normal surface images while the restoration removes defects to generate normal images. It employs a novel compositional layer-based architecture for generating realistic defects within various image backgrounds with different textures and appearances. It can also mimic the stochastic variations of defects and offer flexible control over the locations and categories of the generated defects within the image background. Extensive experiments show that Defect-GAN is capable of synthesizing various defects with superior diversity and fidelity. In addition, the synthesized defect samples demonstrate their effectiveness in training better defect inspection networks.

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Crossref · 10.1109/wacv48630.2021.00257

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Research works printed but not linked: 4

  1. yang, 2017, Deep concrete inspection using unmanned aerial vehicle towards CSSC database, IEEE/RSJ International Conference on Intelligent Robots and Systems
  2. 10.1109/CVPR.2017.632 · DOI · Paper page
  3. 10.1109/CVPR.2018.00131 · DOI
  4. 10.1109/ICCV.2019.00460 · DOI

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Metadata

Authors
Zhang, G., Cui, K., Hung, T.-Y., & Lu, S.
Affiliations
  • Nanyang Technological University (SG)
  • Delta Research Center,Singapore
ORCID
Venue
2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
Publisher
IEEE
Published
2021-01
Pages
2523-2533
ISSN
2472-6737 (ISSN-L)
DOI
10.1109/wacv48630.2021.00257
OpenAlex
W3120387510
Cited by
200
References
80
Citations by year
2026: 48 · 2025: 59 · 2024: 43 · 2023: 21 · 2022: 22 · 2021: 7
FWCI
31.92
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Research Foundation
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords
automated defect detection, generative adversarial networks, data scarcity, industrial defect detection

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