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 new setting from industrial manufacturing and review the current IAD approaches under our proposed our new 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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• Dual-path Frequency Discriminators for Few-shot Anomaly Detection
• Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillation
• Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
• ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects
• Can I trust my anomaly detection system? A case study based on explainable AI
• Multimodal Industrial Anomaly Detection via Geometric Prior