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Recent advances in surface defect inspection of industrial products using deep learning techniques

Zheng, Xiaoqing, Zheng, Song, Kong, Yaguang, Chen, Jie·2021·The International Journal of Advanced Manufacturing Technology

DOI 10.1007/s00170-021-06592-8

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Authors: Zheng, X., Zheng, S., Kong, Y., & Chen, J.
Affiliations: Hangzhou Dianzi University (CN)
Corresponding author: Zheng, Xiaoqing
ORCID: Zheng, Xiaoqing
Zheng, Song
Kong, Yaguang
Chen, Jie
Venue: The International Journal of Advanced Manufacturing Technology
Publisher: Springer Science and Business Media LLC
Published: 2021-01-25
Volume: 113
Issue: 1-2
Pages: 35-58
ISSN: 0268-3768 (ISSN-L)
1433-3015
DOI: 10.1007/s00170-021-06592-8
OpenAlex: W3124804470
Semantic Scholar: f8a9dcf96f390ae76fb744cc8c3935fa3ddb2468
Cited by: 287
References: 159
Citations by year: 2026: 62 · 2025: 79 · 2024: 68 · 2023: 44 · 2022: 30 · 2021: 4
FWCI: 18.88
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: The International Journal of Advanced Manufacturing Technology (publishedVersion)
Funders: National Natural Science Foundation of China (U1609212)
Topics: Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords: surface defect detection, automated visual inspection, machine vision, deep learning, industrial inspection, semiconductor manufacturing, steel industry, textile industry, defect detection algorithm

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