Industrial Anomaly Detection

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Overview

Industrial anomaly detection aims to identify defects or unusual behavior when examples of failures are scarce and normal operation can vary across products, machines, and conditions. Visual methods often learn normal patterns with reconstruction models, patch-memory methods such as PatchCore, or pretrained vision features; synthetic defects and vision-language models are increasingly used to extend detection to new defect types. For sensor and audio data, common approaches include forecasting and reconstruction, autoencoders, and graph models that capture relationships among signals, with benchmarks such as MVTec AD, VisA, SWaT, and WADI. A central challenge is moving from strong results on curated benchmarks to reliable deployment under shifts, contaminated training data, tight false-alarm limits, and edge-device constraints.

Latest papers

CardsList

261 papers33 papers in the last four weeks, up 38% on the four weeks before. 0.2% of all new papers.

  1. Data Collection for Training Quality-Control AI in Carpet Manufacturing

    May 31, 2026Akbar ErkinovIndustrial Anomaly DetectionIndustrial Inspection

    Independent Researcher

  2. RefDiffNet: Learning to Expose Subtle PCB Defects Before Detection

    May 30, 2026Vinay Edula, Nilesh Badwe, Priyanka BagadeIndustrial Anomaly DetectionIndustrial Inspection

    Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur, India · Department of Materials Science and Engineering Indian Institute of Technology Kanpur Kanpur, India

  3. Training-Free Object-Agnostic Jam Detection in Fulfillment Centers

    May 29, 2026Ruiliang Liu, Tina Dongxu Li, Joshua Migdal +3Industrial Anomaly DetectionVideo Anomaly Detection

    Amazon, USA

  4. Enhancing Computer Vision Model Generalization in Warehouse Facilities: A Case Study on Anomaly Detection in Vertical Material Handling Systems

    May 29, 2026Ruiliang Liu, Tina Dongxu Li, Joshua Migdal +2Domain GeneralizationIndustrial Anomaly Detection

    Amazon, USA

  5. Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source

    May 29, 2026Alberto D. Cencillo, Leonardo Concepción, Julián Luengo +1Industrial Anomaly DetectionConvolutional Neural Networks

    Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) · Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Granada, 18071, Spain

  6. Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes

    May 28, 2026Chenxi Tao, Seung-Kyum ChoiSynthetic-to-Real Domain AdaptationSim-to-Real Transfer

    George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology

  7. AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection

    May 28, 2026Yi Zhang, Jiawen Zhu, Lele Fu +1Few-Shot Anomaly DetectionTool Use in VLMs

    Singapore Management University, Singapore · Sun Yat-sen University, China

  8. Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection

    May 28, 2026Yangchen Wu, Huiqiang XieIndustrial Anomaly DetectionAnomaly Localization

    School of Information Science and Technology, Jinan University, Guangzhou, Guangdong 510632, P. R. China

  9. EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection

    May 27, 2026Xinyu Zhao, Qingyun Sun, Jiayi Luo +1VLM AdaptationMedical Image Anomaly Detection

    Beihang University Beijing, China

  10. Mahalanobis PatchCore: Covariance-Aware and Streaming-Compatible Industrial Anomaly Detection

    May 26, 2026Niccolò Ferrari, Oligert Osmani, Evelina LammaMahalanobis DistanceIndustrial Anomaly Detection

    Department of Engineering, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy · NAIS s.r.l., Via Maria Callas 4, 40131, Bologna, Italy · Bonfiglioli Engineering S.r.l., Via Amerigo Vespucci 20, 44124, Ferrara, Italy

  11. Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation

    May 26, 2026Khayyam Nosrati, Martin Uray, Saverio Messineo +2Industrial Anomaly DetectionTime-Series Anomaly Detection

    Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences, Austria · Department of Artificial Intelligence and Human Interfaces, Paris Lodron University of Salzburg, Austria

  12. Memory-Distilled Selection for Noise-Robust Anomaly Detection

    May 26, 2026Sirojbek Safarov, Jaewoo Park, Yoon Gyo Jung +4Industrial Anomaly DetectionData Selection

    AIVEX Inc. · Northeastern University · Mitsubishi Electric Research Laboratories (MERL).

  13. Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection

    May 26, 2026Yuxuan Yin, Chen He, Todd Jacobs +4Semiconductor ManufacturingIndustrial Anomaly Detection

    Department of Electrical and Computer Engineering, University of California Santa Barbara, CA, USA · Automotive Processing, NXP Semiconductors, TX, USA

  14. Towards Active Real-to-Twin Inspection: A New Paradigm for Zero-Shot Anomaly Detection

    May 25, 2026Jiaxuan Liu, Yunkang Cao, Yufeng Chen +3Industrial Anomaly DetectionAnomaly Localization

    National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, Hunan, China

  15. ASTRO: Adaptive Spatio-Temporal Reinforcement Optimization for GNN Powered Anomly Detection in Cyber Physical Systems

    May 24, 2026Rai Ali Yar, Umaisa Lail, Anwar ShahIndustrial Anomaly DetectionOnline Anomaly Detection

    Department of Computer Science, FAST NUCES, Development Sector, Faisalabad, Pakistan · Department of Information Technology, Riphah International University, Faisalabad, Pakistan

  16. Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces

    May 23, 2026Yaoxuan Feng, Yuxin Li, Weijiang Lv +5Prototype LearningIndustrial Anomaly Detection

    National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, 710071, China.

  17. Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions

    May 22, 2026Chad Weatherly, Sen LinContinual Anomaly DetectionEdge Inference

    University of Houston

  18. Rethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks

    May 22, 2026Mehdi Gharbage, Céline Teulière, Pierre Bouges +1Self-Supervised Pre-TrainingIndustrial Anomaly Detection

    1Michelin Tyres Manufacturer · 2Universit´e Clermont Auvergne, CNRS, Institut Pascal, Clermont-Ferrand, France

  19. Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models

    May 21, 2026Shengzhe Chen, Mehrdad Moradi, Kamran Paynabar +1Flow MatchingIndustrial Anomaly Detection

    1Arizona State University · 2Georgia Institute of Technology

  20. IndusAgent: Agentic Reinforcement Learning for Cross-Category Industrial Anomaly Detection

    May 20, 2026Rongbin Tan, Fangfang Lin, Jinguo Luo +9Industrial Anomaly DetectionTool-Using Vision-Language Agents

    State Key Lab of Processors, Institute of Computing Technology, CAS · University of Chinese Academy of Sciences, Beijing, China · Santa Clara University +5

  21. JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA

    May 19, 2026Hyunju Kang, Woohyun Lee, Jaewon Kim +1Industrial Anomaly DetectionAnomaly Localization

    Sungkyunkwan University · Seoul National University

  22. UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction

    May 18, 2026Robson W. S. Pessoa, Julien Amblard, Alessandra Russo +1Multivariate Time Series ForecastingIndustrial Anomaly Detection

    aDepartment of Chemical Engineering, Norwegian University of Science and Technology2026 (NTNU), Trondheim, Norway · bDepartment of Computing, Imperial College London, London, United Kingdom

  23. Ti-iLSTM: A TinyDL Approach for Logic-Level Anomaly Detection in Industrial Water Treatment Systems

    May 15, 2026Mandar Joshi, Farzana Zahid, Judy Bowen +3Efficient Neural Network InferenceIndustrial Anomaly Detection

    University of Waikato, Hamilton, New Zealand · Auckland University of Technology, Auckland, New Zealand · Aalto University, Espoo, Finland

  24. Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection

    May 15, 2026Heqiang Wang, Weihong Yang, Zheyuan Yang +4Industrial Anomaly DetectionMultimodal Anomaly Detection

    Pengcheng Laboratory, Shenzhen, 518066, China · Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China

  25. From Data to Action: Accelerating Refinery Optimization with AI

    May 14, 2026Dániel Pfeifer, Ábrahám Papp, Tibor Bernáth +4Industrial Anomaly DetectionUnsupervised Anomaly Detection

    aBudapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, 1111, Hungary · bMOL Group, Dombóvári út 28., Budapest, 1117, Hungary

  26. SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track

    May 14, 2026Lukas Roming, Felix Lehnerer, Jonas V. Funk +4OOD GeneralizationIndustrial Anomaly Detection

    Fraunhofer IOSB, Fraunhoferstr. 1, 76131 Karlsruhe, Germany · Independent Research

  27. Product-Aware Deep Autoencoders for Robust Process Monitoring in Multi-Product Cyber-Physical Systems

    May 13, 2026MD Shafikul Islam, Jordan CardenCyber-Physical SystemsAutoencoders

  28. Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection

    May 12, 2026Muhammad Aqeel, Maham Nazir, Uzair Khan +2VLM AdaptationMedical Image Anomaly Detection

    Dept. of Engineering for Innovation Medicine, University of Verona, Italy · School of Computer Science and Engineering, Beihang University, China · Dept. of Engineering for Innovation +1

  29. MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection

    May 11, 2026Xiran Zhao, Jing Jin, Yan Bai +6Multi-View LearningIndustrial Anomaly Detection

    ShanghaiTech University · Tsinghua University · Meituan Inc. +1

  30. Hypergraph-Enhanced Training-Free and Language-Free Few-Shot Anomaly Detection

    May 11, 2026Guohuan Xie, Xin He, Dingying Fan +2Few-Shot Anomaly DetectionMedical Image Anomaly Detection

    Nankai University · Tianjin University of Technology · Tsinghua University