工业图像表面异常定位的无监督学习方法综述

doi:10.3778/j.issn.1002-8331.2503-0133

摘要/Abstract

摘要： 深度学习的快速发展为工业图像的异常检测和定位奠定了里程碑，现有研究对全面深入探索该领域具体方法和新兴趋势的需求不断增长，超越了传统的监督训练范式。探讨了基于自监督和无监督学习的异常定位方法的背景动机、发展现状和核心挑战，从神经网络架构设计、特殊应用场景分析、损失函数改进、评价指标和公开数据集的使用情况等角度全面回顾了工业领域现有重要研究。重点研究了少样本学习下，大型视觉语言模型对多类别统一异常定位任务的认知和推理作用，总结了现有研究成果并指出了未来的研究方向，旨在促进利用大模型的能力来增强复杂真实场景中异常定位算法的稳健性和系统开发的高效性。这项全面的分析旨在弥合现有的知识差距，为研究人员提供宝贵的见解，并为塑造工业异常定位研究的未来作出贡献。

关键词: 智能制造, 工业图像处理, 异常检测与定位, 深度学习, 无监督学习

Abstract: The rapid development of deep learning has marked a milestone for anomaly detection and localization in industrial images. The demand for comprehensive and in-depth exploration of specific methods and emerging trends in this field continues to grow in existing research, surpassing traditional supervised training paradigms. The background, current developments, and key challenges of anomaly localization methods based on self-supervised and unsupervised learning are discussed. The review offers a comprehensive analysis of existing outstanding research in the industrial domain, addressing aspects such as neural network architecture design, special application scenarios, loss function improvements, collection of public datasets, and the use of evaluation metrics. Furthermore, this paper focuses on the role of large visual-language models in few-shot learning for multi-class unified anomaly localization tasks, exploring their cognitive and reasoning capabilities. It summarizes current research findings and highlights future research directions, aiming to enhance the robustness of anomaly localization algorithms in complex real-world scenarios and improve the efficiency of system development. This comprehensive analysis seeks to bridge existing knowledge gaps, provide valuable insights for researchers, and contribute to shaping the future of industrial anomaly localization research.

Key words: intelligent manufacturing, industrial image processing, anomaly detection and localization, deep learning, unsupervised learning

赵俊, 赵涓涓. 工业图像表面异常定位的无监督学习方法综述[J]. 计算机工程与应用, 2025, 61(23): 38-58.

ZHAO Jun, ZHAO Juanjuan. Review of Unsupervised Learning Methods for Surface Anomaly Localization in Industrial Images[J]. Computer Engineering and Applications, 2025, 61(23): 38-58.

参考文献

[1] ZHANG H, WU Z X, WANG Z, et al. Prototypical residual networks for anomaly detection and localization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 16281-16291.
[2] 陶显, 侯伟, 徐德. 基于深度学习的表面缺陷检测方法综述[J]. 自动化学报, 2021, 47(5): 1017-1034.
TAO X, HOU W, XU D. A survey of surface defect detection methods based on deep learning[J]. Acta Automatica Sinica, 2021, 47(5):1017-1034.
[3] AIGER D, TALBOT H. The phase only transform for unsupervised surface defect detection[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2010: 295-302.
[4] XIE X H, MIRMEHDI M. TEXEMS: texture exemplars for defect detection on random textured surfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(8): 1454-1464.
[5] MOKOENA T, CELIK T, MARIVATE V. Why is this an anomaly?explaining anomalies using sequential explanations[J]. Pattern Recognition, 2022, 121: 108227.
[6] MAKARYCHEV K, SHAN L. Near-optimal algorithms for explainable k-medians and k-means[C]//Proceedings of the International Conference on Machine Learning, 2021: 7358-7367.
[7] PUTINA A, SOZIO M, ROSSI D, et al. Random histogram forest for unsupervised anomaly detection[C]//Proceedings of the IEEE International Conference on Data Mining. Piscataway: IEEE, 2020: 1226-1231.
[8] RUFF L, VANDERMEULEN R A, G?RNITZ N, et al. Deep semi-supervised anomaly detection[J]. arXiv:1906.02694, 2019.
[9] DONG H W, SONG K C, HE Y, et al. PGA-Net: pyramid feature fusion and global context attention network for automated surface defect detection[J]. IEEE Transactions on Industrial Informatics, 2020, 16(12): 7448-7458.
[10] LIAN J, JIA W K, ZAREAPOOR M, et al. Deep-learning-based small surface defect detection via an exaggerated local variation-based generative adversarial network[J]. IEEE Transactions on Industrial Informatics, 2020, 16(2): 1343-1351.
[11] SU B Y, CHEN H Y, CHEN P, et al. Deep learning-based solar-cell manufacturing defect detection with complementary attention network[J]. IEEE Transactions on Industrial Informatics, 2021, 17(6): 4084-4095.
[12] DING C B, PANG G S, SHEN C H. Catching both gray and black swans: open-set supervised anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 7378-7388.
[13] LIU Y H, GAO X D, WEN J Z, et al. Unsupervised image anomaly detection and localization in industry based on self-updated memory and center clustering[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-10.
[14] CHOW J K, SU Z, WU J, et al. Anomaly detection of defects on concrete structures with the convolutional autoencoder[J]. Advanced Engineering Informatics, 2020, 45: 101105.
[15] MEI S, YANG H, YIN Z P. An unsupervised-learning-based approach for automated defect inspection on textured surfaces[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(6): 1266-1277.
[16] AKCAY S, ATAPOUR-ABARGHOUEI A, BRECKON T P. GANomaly: semi-supervised anomaly detection via adversarial training[C]//Proceedings of the 14th Asian Conference on Computer Vision, 2019: 622-637.
[17] AKCAY S, ATAPOUR-ABARGHOUEI A, BRECKON T P. Skip-GANomaly: skip connected and adversarially trained encoder-decoder anomaly detection[C]//Proceedings of the International Joint Conference on Neural Networks. Piscataway: IEEE, 2019: 1-8.
[18] LIANG Y, ZHANG J, ZHAO S, et al. Omni-frequency channel-selection representations for unsupervised anomaly detection[J]. IEEE Transactions on Image Processing, 2023, 32: 4327-4340.
[19] MOU S, GU X, CAO M, et al. RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection[C]//Proceedings of the 11th International Conference on Learning Representations, 2023:1-28.
[20] ZHANG J, WANG C, LI X, et al. Learning feature inversion for multi-class anomaly detection under general-purpose COCO-AD benchmark[J]. arXiv:2404.10760, 2024.
[21] HO J, JAIN A, ABBEEL P. Denoising diffusion probabilistic models[C]//Advances in Neural Information Processing Systems, 2020: 6840-6851.
[22] SONG J, MENG C, ERMON S. Denoising diffusion implicit models[J]. arXiv:2010.02502, 2020.
[23] ROMBACH R, BLATTMANN A, LORENZ D, et al. High-resolution image synthesis with latent diffusion models[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 10674-10685.
[24] LIU W Z, LIU C, LIU Q, et al. Assigned MURA defect generation based on diffusion model[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2023: 4395-4402.
[25] ZHANG X Y, LI N Q, LI J W, et al. Unsupervised surface anomaly detection with diffusion probabilistic model[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 6759-6768.
[26] YAO H, LIU M, WANG H L, et al. GLAD: towards better reconstruction with global and local adaptive diffusion models for unsupervised anomaly detection[J]. arXiv:2406. 07487, 2024.
[27] WANG J P, XU G L, LI C L, et al. Multi-feature reconstruction network using crossed-mask restoration for unsupervised anomaly detection[J]. arXiv:2404.13273, 2024.
[28] YI J, YOON S. Patch SVDD: patch-level SVDD for anomaly detection and segmentation[C]//Proceedings of the Asian Conference on Computer Vision, 2020: 375-390.
[29] WANG S Z, WU L W, CUI L, et al. Glancing at the patch: anomaly localization with global and local feature comparison[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 254-263.
[30] PIRNAY J, CHAI K. Inpainting transformer for anomaly detection[C]//Proceedings of the ?International Conference on Image Analysis and Processing?. Cham: Springer, 2022: 394-406.
[31] JIN W, GUO F, ZHU L. ISSTAD: incremental self-supervised learning based on transformer for anomaly detection and localization[J]. arXiv:2303.17354, 2023.
[32] ZAVRTANIK V, KRISTAN M, SKO?AJ D. Reconstruction by inpainting for visual anomaly detection[J]. Pattern Recognition, 2021, 112: 107706.
[33] LIU T, LI B, DU X, et al. Fair: frequency-aware image restoration for industrial visual anomaly detection[J]. arXiv:2309.07068, 2023.
[34] LUO W, YAO H M, YU W Y. Normal reference attention and defective feature perception network for surface defect detection[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-14.
[35] TAO X, ADAK C, CHUN P J, et al. ViTALnet: anomaly on industrial textured surfaces with hybrid transformer[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-13.
[36] YANG M H, WU P, FENG H. MemSeg: a semi-supervised method for image surface defect detection using differences and commonalities[J]. Engineering Applications of Artificial Intelligence, 2023, 119: 105835.
[37] ZHANG L R, ZHANG S H, XIE G Y, et al. What makes a good data augmentation for few?shot unsupervised image anomaly detection?[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2023: 4345-4354.
[38] LI C L, SOHN K, YOON J, et al. CutPaste: self-supervised learning for anomaly detection and localization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 9659-9669.
[39] ZOU Y, JEONG J, PEMULA L, et al. SPot-the-difference self-supervised pre-training for anomaly detection and segmentation[C]//Proceedings of the European Conference on Computer Vision. Cham: Springer, 2022: 392-408.
[40] SCHLüTER H M, TAN J, HOU B, et al. Natural synthetic anomalies for self-supervised anomaly detection and localization[C]//Proceedings of the European Conference on Computer Vision. Cham: Springer, 2022: 474-489.
[41] CHIU L L, LAI S-H. Self-supervised normalizing flows for image anomaly detection and localization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2023: 2927-2936.
[42] ZAVRTANIK V, KRISTAN M, SKOCAJ D. DRAEM—a discriminatively trained reconstruction embedding for surface anomaly detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 8310-8319.
[43] TAO X, ZHANG D P, MA W Z, et al. Unsupervised anomaly detection for surface defects with dual-Siamese network[J]. IEEE Transactions on Industrial Informatics, 2022, 18(11): 7707-7717.
[44] LYU S, MO D M, WONG W K. REB: reducing biases in representation for industrial anomaly detection[J]. Knowledge-Based Systems, 2024, 290: 111563.
[45] LIU Z K, ZHOU Y M, XU Y S, et al. SimpleNet: a simple network for image anomaly detection and localization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 20402-20411.
[46] BAI Y, ZHANG J, DONG Y, et al. Dual-path frequency discriminators for few-shot anomaly detection[J]. arXiv:2403.
04151, 2024.
[47] ZAVRTANIK V, KRISTAN M, SKO?AJ D. Cheating depth: enhancing 3D surface anomaly detection via depth simulation[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2024: 2153-2161.
[48] YANG M, LIU J, YANG Z, et al. SLSG: industrial image anomaly detection by learning better feature embeddings and one-class classification[J]. arXiv:2305.00398, 2023.
[49] ZHANG X M, XU M, ZHOU X Z. RealNet: a feature selection network with realistic synthetic anomaly for anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2024: 16699-16708.
[50] ZAVRTANIK V, KRISTAN M, SKO?AJ D. DSR—a dual subspace re-projection network forSurface anomaly detection[C]//Proceedings of the European Conference on Computer Vision. Cham: Springer, 2022: 539-554.
[51] ZHAO Y. Just noticeable learning for unsupervised anomaly localization and detection[C]//Proceedings of the IEEE International Conference on Multimedia and Expo. Piscataway: IEEE, 2022: 01-06.
[52] ZHAO Y. OmniAL: a unified CNN framework for unsupervised anomaly localization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 3924-3933.
[53] CHEN Q, LUO H, LV C, et al. A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization[J]. arXiv:2407.09359, 2024.
[54] YAO H M, LUO W, YU W Y. Visual anomaly detection via dual?attention transformer and discriminative flow[J]. arXiv:2303.17882, 2023.
[55] BERGMANN P, FAUSER M, SATTLEGGER D, et al. Uninformed students: student-teacher anomaly detection with discriminative latent embeddings[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 4182-4191.
[56] WANG G, HAN S, DING E, HUANG D. Student-teacher feature pyramid matching for anomaly detection[J]. arXiv:2103.04257, 2021.
[57] SALEHI M, SADJADI N, BASELIZADEH S, et al. Multiresolution knowledge distillation for anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 14897-14907.
[58] ZHOU Q H, HE S B, LIU H Y, et al. Pull & push: leveraging differential knowledge distillation for efficient unsupervised anomaly detection and localization[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(5): 2176-2189.
[59] RUDOLPH M, WEHRBEIN T, ROSENHAHN B, et al. Asymmetric student-teacher networks for industrial anomaly detection[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2023: 2591-2601.
[60] ZHANG X, LI S Y, LI X, et al. DeSTSeg: segmentation guided denoising student-teacher for anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 3914-3923.
[61] CAI Y X, LIANG D K, LUO D L, et al. A discrepancy aware framework for robust anomaly detection[J]. IEEE Transactions on Industrial Informatics, 2024, 20(3): 3986-3995.
[62] DENG H Q, LI X Y. Anomaly detection via reverse distillation from one?class embedding[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 9727-9736.
[63] TONG G X, LI Q Q, SONG Y. Two-stage reverse knowledge distillation incorporated and self-supervised masking strategy for industrial anomaly detection[J]. Knowledge-Based Systems, 2023, 273: 110611.
[64] GUDOVSKIY D, ISHIZAKA S, KOZUKA K. CFLOW-AD: real-time unsupervised anomaly detection with localization via conditional normalizing flows[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2022: 1819-1828.
[65] LEI J R, HU X B, WANG Y, et al. PyramidFlow: high-resolution defect contrastive localization using pyramid normalizing flow[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 14143-14152.
[66] LUO A S, WEN G J, CHENG Y H, et al. DMMGNet: a discrimination mapping and memory bank mean guidance-based network for high-performance few-shot industrial anomaly detection[J]. Neurocomputing, 2024, 610: 128622.
[67] WANG Y, PENG J L, ZHANG J N, et al. Multimodal industrial anomaly detection via hybrid fusion[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 8032-8041.
[68] CAI X Y, XIAO R L, ZENG Z X, et al. ITran: a novel transformer-based approach for industrial anomaly detection and localization[J]. Engineering Applications of Artificial Intelligence, 2023, 125: 106677.
[69] HE H Y, BAI Y H, ZHANG J N, et al. MambaAD: exploring state space models for multi-class unsupervised anomaly detection[J]. arXiv:2404.06564, 2024.
[70] LI X F, ZHANG Z Z, TAN X, et al. PromptAD: learning prompts with only normal samples for few-shot anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2024: 16848-16858.
[71] JANG J, HWANG E, PARK S H. N-pad: neighboring pixel-based industrial anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2023: 4365-4374.
[72] CHEN L Y, YOU Z Y, ZHANG N, et al. UTRAD: anomaly detection and localization with U-Transformer[J]. Neural Networks, 2022, 147: 53-62.
[73] RUFF L, VANDERMEULEN R, GOERNITZ N, et al. Deep one-class classification[C]//Proceedings of the International Conference on Machine Learning, 2018: 4393-4402.
[74] WU J C, CHEN D J, FUH C S, et al. Learning unsupervised MetaFormer for anomaly detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 4349-4358.
[75] HUANG C Q, GUAN H Y, JIANG A F, et al. Registration based few?shot anomaly detection[C]//Proceedings of the European Conference on Computer Vision. Cham: Springer, 2022: 303-319.
[76] DEFARD T, SETKOV A, LOESCH A, et al. PaDiM: a patch distribution modeling framework for anomaly detection and localization[C]//Proceedings of the International Conference on Pattern Recognition. Cham: Springer, 2021: 475-489.
[77] RUDOLPH M, WANDT B, ROSENHAHN B. Same same but DifferNet: semi-supervised defect detection with normalizing flows[C]//Proceedings of the IEEE Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2021: 1906-1915.
[78] ROTH K, PEMULA L, ZEPEDA J, et al. Towards total recall in industrial anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 14298-14308.
[79] XIE G, WANG J, LIU J, et al. Pushing the limits of fewshot anomaly detection in industry vision: Graphcore[J]. arXiv:2301.12082, 2023.
[80] JEONG J, ZOU Y, KIM T, et al. WinCLIP: zero-/few-shot anomaly classification and segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 19606-19616.
[81] TAMURA M. Random word data augmentation with clip for zero-shot anomaly detection[J]. arXiv:2308.11119, 2023.
[82] ZHU J W, PANG G S. Toward generalist anomaly detection via in?context residual learning with few?shot sample prompts[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2024: 17826-17836.
[83] TAO F F, XIE G S, ZHAO F, et al. Kernel-aware graph prompt learning for few?shot anomaly detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2025: 7347-7355.
[84] SHEYNIN S, BENAIM S, WOLF L. A hierarchical transformation-discriminating generative model for few shot anomaly detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 8475-8484.
[85] FANG Z, WANG X Y, LI H C, et al. FastRecon: few-shot industrial anomaly detection via fast feature reconstruction[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 17435-17444.
[86] YAN Z, FANG Q, LYU W, et al. AnomalySD: few-shot multi-class anomaly detection with stable diffusion model[J]. arXiv:2408.01960, 2024.
[87] LI Y, ZHANG S, LI K, et al. One-to-Normal: anomaly personalization for few-shot anomaly detection[C]//Advances in Neural Information Processing Systems, 2024: 78371-78393.
[88] HU T, ZHANG J N, YI R, et al. AnomalyDiffusion: few-shot anomaly image generation with diffusion model[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2024: 8526-8534.
[89] JIN Y, PENG J, HE Q, et al. DualAnoDiff: dual-interrelated diffusion model for few-shot anomaly image generation[J]. arXiv:2408.13509, 2024.
[90] HE S, LIU L, SHU X, et al. Anomalycontrol: learning cross-modal semantic features for controllable anomaly synthesis[J]. arXiv:2412.06510, 2024.
[91] DENG H, ZHANG Z, BAO J, et al. AnoVL: adapting vision-language models for unified zero-shot anomaly localization[J]. arXiv:2308.15939, 2023.
[92] CHEN X, ZHANG J, TIAN G, et al. CLIP-AD: a language-guided staged dual-path model for zero-shot anomaly detection[J]. arXiv:2311.00453, 2023.
[93] LI Y, WANG H L, DUAN Y Q, et al. CLIP surgery for better explainability with enhancement in open-vocabulary tasks[J]. arXiv:2304.05653, 2023.
[94] ZHOU Q, PANG G, TIAN Y, et al. AnomalyCLIP: object-agnostic prompt learning for zero-shot anomaly detection[J]. arXiv:2310.18961, 2023.
[95] ZHU J, CAI S, DENG F, et al. Do LLMs understand visual anomalies? uncovering LLM capabilities in zero-shot anomaly detection[J]. arXiv:2404.09654, 2024.
[96] ZHANG J, CHEN X, XUE Z, et al. Exploring grounding potential of VQA-oriented GPT-4V for zero-shot anomaly detection[J]. arXiv:2311.02612, 2023.
[97] LI Y, WANG H, YUAN S, et al. Myriad: large multimodal model by applying vision experts for industrial anomaly detection[J]. arXiv:2310.19070, 2023.
[98] GU Z P, ZHU B K, ZHU G B, et al. AnomalyGPT: detecting industrial anomalies using large vision-language models[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2024: 1932-1940.
[99] YOU Z, CUI L, SHEN Y, et al. A unified model for multi-class anomaly detection[C]//Advances in Neural Information Processing Systems, 2022: 4571-4584.
[100] HE L, JIANG Z, PENG J, et al. Learning unified reference representation for unsupervised multi-class anomaly detection[J]. arXiv:2403.11561, 2024.
[101] YAO H, LUO W, LOU J, et al. Scalable industrial visual anomaly detection with partial semantics aggregation vision transformer[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 73: 1-17.
[102] HE H Y, ZHANG J N, CHEN H X, et al. A diffusion-based framework for multi-class anomaly detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2024: 8472-8480.
[103] CAI Y, HE X, LIANG D, et al. Anomaly detection by adapting a pre-trained vision language model[J]. arXiv:2403.09493, 2024.
[104] QU Z, TAO X, PRASAD M, et al. VCP-CLIP: a visual context prompting model for zero-shot anomaly segmentation[J]. arXiv:2407.12276, 2024.
[105] 曲海成, 林俊杰. 基于归一化流的多模态多尺度工业场景缺陷检测[J]. 中国图象图形学报, 2025, 30(2):451-466.
QU H C, LIN J J. Multimodal multiscale industrial anomaly detection via flows[J]. Journal of Image and Graphics, 2025, 30(2):451-466.
[106] BERGMANN P, SATTLEGGER D. Anomaly detection in 3D point clouds using deep geometric descriptors[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2023: 2612-2622.
[107] PANG Y T, WANG W X, TAY F E H, et al. Masked autoencoders for point cloud self-supervised learning[C]//Proceedings of the European Conference on Computer Vision. Cham: Springer, 2022: 604-621.
[108] BI C Y, LI Y Y, LUO H C. Dual-branch reconstruction network for industrial anomaly detection with RGB-D data[J]. arXiv:2311.06797, 2023.
[109] BERGMANN P, FAUSER M, SATTLEGGER D, et al. MVTec AD: a comprehensive real-world dataset for unsupervised anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 9584-9592.
[110] MISHRA P, VERK R, FORNASIER D, et al. VT-ADL: a vision transformer network for image anomaly detection and localization[C]//Proceedings of the IEEE 30th International Symposium on Industrial Electronics. Piscataway: IEEE, 2021: 1-6.
[111] JEZEK S, JONAK M, BURGET R, et al. Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions[C]//Proceedings of the 13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops. Piscataway: IEEE, 2021: 66-71.
[112] HUANG Y B, QIU C Y, YUAN K. Surface defect saliency of magnetic tile[J]. The Visual Computer, 2020, 36(1): 85-96.
[113] BERGMANN P, BATZNER K, FAUSER M, et al. Beyond dents and scratches: logical constraints in unsupervised anomaly detection and localization[J]. International Journal of Computer Vision, 2022, 130(4): 947-969.
[114] BO?I? J, TABERNIK D, SKO?AJ D. Mixed supervision for surface-defect detection: from weakly to fully supervised learning[J]. Computers in Industry, 2021, 129: 103459.
[115] WIELER M, HAHN T. Weakly supervised learning for industrial optical inspection[EB/OL]. (2007-12-14)[2024-10-11]. https://github.com/vivekrajsalokhe/weakly-supervised-
learning-for-industrial-optical-inspection.
[116] WANG C J, ZHU W B, GAO B B, et al. Real-IAD: a real-world multi-view dataset for benchmarking versatile industrial anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2024: 22883-22892.
[117] BERGMANN P, JIN X, SATTLEGGER D, et al. The MVTec 3D-AD dataset for unsupervised 3D anomaly detection and localization[J]. arXiv:2112.09045, 2021.
[118] LIU J, XIE G, CHEN R, et al. Real3D-AD: a dataset of point cloud anomaly detection[J]. arXiv:2309.13226, 2023.
[119] BONFIGLIOLI L, TOSCHI M, SILVESTRI D, et al. The eyecandies dataset for unsupervised multimodal anomaly detection and localization[C]//Proceedings of the European Conference on Computer Vision. Cham: Springer, 2023: 459-475.