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Deep Industrial Image Anomaly Detection: A Survey

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  • Open access
  • Published: 15 January 2024
  • Volume 21, pages 104–135, (2024)
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Machine Intelligence Research Aims and scope Submit manuscript
Deep Industrial Image Anomaly Detection: A Survey
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  • Jiaqi Liu  ORCID: orcid.org/0000-0002-2153-84111 na1,
  • Guoyang Xie  ORCID: orcid.org/0000-0001-8433-81531,2 na1,
  • Jinbao Wang  ORCID: orcid.org/0000-0001-5916-89651 na1,
  • Shangnian Li1,
  • Chengjie Wang3,
  • Feng Zheng  ORCID: orcid.org/0000-0002-1701-91411 &
  • …
  • Yaochu Jin  ORCID: orcid.org/0000-0003-1100-06312,4 

  • 12k Accesses

  • 37 Citations

  • 3 Altmetric

  • Explore all metrics

Abstract

The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the promising setting from industrial manufacturing and review the current IAD approaches under our proposed setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

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Acknowledgements

This work was partly supported by the National Key R&D Program of China (No. 2022YFF1202903) and National Natural Science Foundation of China (Nos. 62122035 and 62206122). Y. Jin is funded by an Alexander von Humboldt Professorship for Artificial Intelligence endowed by the Federal Ministry of Education and Research of Germany. Open Access funding provided by Bielefeld University.

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  1. These authors contribute equally to this work

Authors and Affiliations

  1. Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, 518055, China

    Jiaqi Liu, Guoyang Xie, Jinbao Wang, Shangnian Li & Feng Zheng

  2. NICE Group, University of Surrey, Guildford, GU2 7YX, UK

    Guoyang Xie & Yaochu Jin

  3. Youtu Lab, Tencent, Shanghai, 200233, China

    Chengjie Wang

  4. NICE Group, Bielefeld University, Bielefeld, 33619, Germany

    Yaochu Jin

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  1. Jiaqi Liu
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Jiaqi Liu received the B.Sc. degree in software engineering from Dalian University of Technology, China in 2019. He is a master student in electronic science and technology from Southern University of Science and Technology, China, under the supervision of Professor Feng Zheng.

His research interest is image anomaly detection.

Guoyang Xie received the B.Sc. degree in physical electronic and the M. Sc. degree in robotics from University of Electronic Science and Technology of China, Hong Kong University of Science and Technology, China in 2009 and 2013, respectively. He is a Ph. D. degree candidate in machine learning from University of Surrey, UK. Prior to that, he was the Principle Perception Algorithm Engineer in Baidu and GAC, respectively.

His research interests include anomaly detection, medical imaging, neural architecture search and federated learning.

Jinbao Wang received the Ph.D. degree in computer application technology from University of Chinese Academy of Sciences (UCAS), China in 2019. He is currently a research assistant professor with Southern University of Science and Technology (SUSTech), China.

His research interests include machine learning, computer vision, image anomaly detection, and graph representation learning.

Shangnian Li received the B. Sc. in measurement and control technology and instruments and the M. Sc. degree in computer science and technology from Huaiyin Institute of Technology, Beijing Union University, China in 2012 and 2016, respectively. He is currently the research assistant of Sustech VIP Lab, China. Prior to that, he was the vehicle networking engineer in Beijing Xiangzhi Technology Co., Ltd., China.

His research interests include internet of things and anomaly detection.

Chengjie Wang received the B.Sc. degree in computer science from Shanghai Jiao Tong University, China in 2011, and double M. Sc. degrees in computer science from Shanghai Jiao Tong University, and Waseda University, Japan, in 2014. He is currently the Research Director of YouTu Lab, Tencent, China. And he is pursuing the Ph.D. degree in computer science at Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. He has authored or coauthored more than 90 refereed papers on major Computer Vision and Artificial Intelligence Conferences, such as CVPR, ICCV, ECCV, AAAI, IJCAI, and NeurIPS, and holds more than 100 patents in his research areas.

His research interests include computer vision and machine learning.

Feng Zheng received the Ph.D. degree in electronic science and engineering from the University of Sheffield, UK in 2017. He is currently an assistant professor with Department of Computer Science and Engineering, Southern University of Science and Technology, China.

His research interests include machine learning, computer vision, and human-computer interaction.

Yaochu Jin received the B.Sc, M.Sc. and Ph.D. degrees in automatic control from Zhejiang University, China in 1988, 1991 and 1996, respectively, and the Dr.Ing. degree in computer engineering from Ruhr University Bochum, Germany in 2001. He is presently an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, Chair of Nature Inspired Computing and Engineering, Faculty of Technology, Bielefeld University, Germany. He is also a Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, UK. He was a “Finland Distinguished Professor” of University of Jyvskyl awarded by the Academy of Science and Finnish Funding Agency for Innovation, Finland, and “Changjiang Distinguished Visiting Professor” of Northeastern University, awarded by the Ministry of Education, China. Prof. Jin is the President-Elect of the IEEE Computational Intelligence Society and the Editor-in-Chief of Complex & Intelligent Systems. He was named by Clarivate as a “Highly Cited Researcher” from 2019 to 2022 consecutively. He is a Member of Academia Europaea and Fellow of IEEE.

His research interests include human-centered learning and optimization, synergies between evolution and learning, and evolutionary developmental artificial intelligence.

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Cite this article

Liu, J., Xie, G., Wang, J. et al. Deep Industrial Image Anomaly Detection: A Survey. Mach. Intell. Res. 21, 104–135 (2024). https://doi.org/10.1007/s11633-023-1459-z

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  • Received: 22 March 2023

  • Accepted: 26 May 2023

  • Published: 15 January 2024

  • Issue Date: February 2024

  • DOI: https://doi.org/10.1007/s11633-023-1459-z

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Keywords

  • Image anomaly detection
  • defect detection
  • industrial manufacturing
  • deep learning
  • computer vision.
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