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Cited by 21Industrial Vision Systems and Defect DetectionInfrastructure Maintenance and MonitoringAdvanced Neural Network Applications

A simulation-based few samples learning method for surface defect segmentation

Wei, Taoran, Cao, Danhua, Zheng, Caiyun, Yang, Qun·2020·Neurocomputing
DOI 10.1016/j.neucom.2020.06.090
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References 54 · Cited by 21

Authors
Wei, T., Cao, D., Zheng, C., & Yang, Q.
Affiliations
Huazhong University of Science and Technology (CN)
Corresponding author
Cao, Danhua
ORCID
Venue
Neurocomputing
Publisher
Elsevier BV
Published
2020-10
Volume
412
Pages
461-476
ISSN
0925-2312 (ISSN-L)
DOI
10.1016/j.neucom.2020.06.090
OpenAlex
W3040458218
Cited by
21
References
54
Citations by year
2026: 1 · 2025: 5 · 2024: 8 · 2023: 2 · 2022: 4 · 2021: 1
FWCI
1.12
Type
journal article
Language
en
Open access
closed
Licence
Locations
Neurocomputing (publishedVersion)
Funders
Department of Science and Technology, Hubei Provincial People's Government
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
surface defect segmentation, few-shot learning, generative adversarial networks, neural style transfer, industrial defect detection, texture consistency, road crack detection

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