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Cited by 137Industrial Vision Systems and Defect DetectionNon-Destructive Testing TechniquesInfrastructure Maintenance and Monitoring

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Deep-Learning-Based Small Surface Defect Detection via an Exaggerated Local Variation-Based Generative Adversarial Network

Lian, Jian, Jia, Weikuan, Zareapoor, Masoumeh, Zheng, Yuanjie, Luo, Rong, Jain, Deepak Kumar, Kumar, Neeraj·2020·IEEE Transactions on Industrial Informatics
DOI 10.1109/tii.2019.2945403
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Abstract

Surface detection of small defects plays a vital role in manufacturing and has attracted broad interest. It remains challenging primarily due to the small size of the defect relative to the large surface and the rare occurrence of defects. To address this problem, in this article we propose a novel machine vision approach for automatically identifying the tiny flaws that may appear in a single image. First, the presented defect exaggeration approach produces both the flawless image and the corresponding exaggerated version of the defect by taking the variations in the image as regularization terms. Second, a generative adversarial network (GAN) in conjunction with a convolutional neural network (CNN) is proposed to guarantee the accuracy of tiny surface defect detection by producing exaggerated defect image samples. Furthermore, the limited dataset of the training samples for defect detection is enlarged by exploiting the GAN technique with the variation exaggerated images. To evaluate the performance of our proposed method, we conduct comparison experiments between the state-of-the-art techniques with and without the proposed algorithm as well as comparison experiments between the state-of-the-art techniques and our method. The experimental results on different types of surface image samples demonstrate that the proposed method can significantly improve the performance of the state-of-the-art approaches while achieving a defect detection accuracy of 99.2%.

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Research works printed but not linked: 1

  1. wang, 2018, Development and improvement of deep learning based automated defect detection for sewer pipe inspection using faster R-CNNC, Workshop of the European Group for Intelligent Computing in Engineering, 171

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Metadata

Authors
Lian, J., Jia, W., Zareapoor, M., Zheng, Y., Luo, R., Jain, D. K., & Kumar, N.
Affiliations
  • Shandong Normal University (CN)
  • Shanghai Jiao Tong University (CN)
  • Qilu University of Technology (CN)
  • Chongqing University of Posts and Telecommunications (CN)
  • Thapar Institute of Engineering & Technology (IN)
ORCID
Venue
IEEE Transactions on Industrial Informatics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-02
Volume
16
Issue
2
Pages
1343-1351
ISSN
  • 1551-3203 (ISSN-L)
  • 1941-0050
DOI
10.1109/tii.2019.2945403
OpenAlex
W2978742590
Cited by
137
References
30
Citations by year
2026: 9 · 2025: 20 · 2024: 27 · 2023: 23 · 2022: 24 · 2021: 19 · 2020: 14
FWCI
10.71
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Industrial Informatics (publishedVersion)
Funders
  • National Natural Science Foundation of China (81871508, 61572300, 61773246)
  • Focus on Research and Development Plan in Shandong Province (2019GNC106115)
  • Taishan Scholar Program of Shandong Province of China (TSHW201502038)
  • China Postdoctoral Science Foundation (2018M630797)
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
generative adversarial networks, data augmentation, convolutional neural networks, machine vision, manufacturing quality inspection

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