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
Open accessAbstract
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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“Fully supervised learning methods [6,7], used in deep learning, depend on datasets meticulously labeled by experts for model training. Although effective, this approach poses a significant financial burden on many facilities due to the high labor costs involved.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [6]
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Deep Industrial Image Anomaly Detection: A Survey
Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., & Jin, Y. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1459-z · Cited by 463
“DefectGAN [164] also believes that defects and normal backgrounds can be layered, and that defects are foreground.”
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Region- and Strength-Controllable GAN for Defect Generation and Segmentation in Industrial Images
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Metadata
- Authors
- Zhang, G., Cui, K., Hung, T.-Y., & Lu, S.
- Affiliations
- Nanyang Technological University (SG)
- Delta Research Center,Singapore
- Venue
- 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2021-01
- Pages
- 2523-2533
- ISSN
- 2472-6737 (ISSN-L)
- 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
- green
- arxiv.org
- 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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