mlsanityData to 14 Oct 2026

Deep Industrial Image Anomaly Detection: A Survey

arXiv 2301.11514 ↗First posted 27 Jan 2023cs.CVcited by 148

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Abstract (arXiv)

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.

Key facts. Cited by 148 papers in the map (data through 14 Oct 2026). Builds on 112 of its 198 references that are in the map. First posted on arXiv 27 Jan 2023.

Builds on

112 of 198 references.

References · 112

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50 most cited of 112 All 112 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 14; 2024: 45; 2025: 52; 2026: 37.

148 papers.

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  1. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 139
  2. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  3. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  4. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  5. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  6. 2023CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detectioncited by 58
  7. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  8. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  9. 2023EasyNet: An Easy Network for 3D Industrial Anomaly Detectioncited by 43
  10. 2023Multimodal Industrial Anomaly Detection by Crossmodal Feature Mappingcited by 42
  11. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  12. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
38 more
  1. 2024Unsupervised Continual Anomaly Detection with Contrastively-learned Promptcited by 31
  2. 2024Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detectioncited by 30
  3. 2024GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Featurescited by 29
  4. 2024Large Language Models for Anomaly and Out-of-Distribution Detection: A Surveycited by 27
  5. 2024MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objectscited by 26
  6. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  7. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  8. 2024M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoisingcited by 24
  9. 2024AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusioncited by 21
  10. 2024Supervised Anomaly Detection for Complex Industrial Imagescited by 18
  11. 2024Self-supervised Feature Adaptation for 3D Industrial Anomaly Detectioncited by 16
  12. 2024Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmarkcited by 16
  13. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  14. 2023InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Imagescited by 15
  15. 2024Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Frameworkcited by 15
  16. 2024Long-Tailed Anomaly Detection with Learnable Class Namescited by 14
  17. 2024Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspectivecited by 12
  18. 2024SplatPose+: Real-time Image-Based Pose-Agnostic 3D Anomaly Detectioncited by 11
  19. 2024Learning Multi-view Anomaly Detection with Efficient Adaptive Selectioncited by 9
  20. 2025LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questionscited by 9
  21. 2025ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detectioncited by 9
  22. 2025Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representationcited by 9
  23. 2024RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detectioncited by 8
  24. 2025PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomaliescited by 8
  25. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  26. 2023High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysiscited by 7
  27. 2024Dual-path Frequency Discriminators for Few-shot Anomaly Detectioncited by 7
  28. 2024Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interferencecited by 7
  29. 2024A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detectioncited by 7
  30. 2024ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detectioncited by 7
  31. 2024AnomalyControl: Learning Cross-modal Semantic Features for Controllable Anomaly Synthesiscited by 7
  32. 2025A Survey on Industrial Anomaly Synthesiscited by 7
  33. 2025FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detectioncited by 7
  34. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  35. 2024Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypescited by 5
  36. 2024Blind Localization and Clustering of Anomalies in Texturescited by 5
  37. 2024Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detectioncited by 5
  38. 2024CableInspect-AD: An Expert-Annotated Anomaly Detection Datasetcited by 5

50 most cited of 148 All 148 (JSON)