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Deep Industrial Image Anomaly Detection:A Survey

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The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection(IAD).In this pa-per,we provide a comprehensive review of deep learning-based image anomaly detection techniques,from the perspectives of neural net-work architectures,levels of supervision,loss functions,metrics and datasets.In addition,we extract the promising setting from indus-trial manufacturing and review the current IAD approaches under our proposed setting.Moreover,we highlight several opening chal-lenges 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.

Image anomaly detectiondefect detectionindustrial manufacturingdeep learningcomputer vision

Jiaqi Liu、Guoyang Xie、Jinbao Wang、Shangnian Li、Chengjie Wang、Feng Zheng、Yaochu Jin

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

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

Youtu Lab,Tencent,Shanghai 200233,China

NICE Group,Bielefeld University,Bielefeld 33619,Germany

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National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

2022YFF12029036212203562206122

2024

10.1007/s11633-023-1459-z

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(1)
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