Deep Industrial Image Anomaly Detection: A Survey

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…

• NN
  National Natural Science Foundation of China

  Awards: 62206122, 62122035
• BF
  Bundesministerium für Bildung und Forschung
• UB
  Universität Bielefeld