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. Co-Evolutionary Prompt Optimization with Cross-Category Transfer for Zero-Shot Anomaly Detection

    Aug 29, 2026Sisi Zhu, Changwei Yu, Renshuai Tao +1Zero-Shot Anomaly DetectionPrompt Optimization

    Institute of Information Science, Beijing Jiaotong University, Beijing, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China

  2. NFAD: Nuisance-Filtered Anomaly Detection Under Distribution Shift

    Aug 29, 2026Dat Cao, Son Nghiem, Phan Nguyen +2Anomaly DetectionIndustrial Anomaly Detection

    Korea Advanced Institute of Science and Technology (KAIST), South Korea · Sony Computer Science Laboratories (Sony CSL), Japan · The University of Tokyo, Japan +1

  3. CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection

    Aug 28, 2026Xinda Yu, Kunxin Zheng, Chunan Yu +4Industrial Anomaly DetectionReal-Time Object Detection

    School of Information Engineering, Huzhou University, Huzhou, 313000, China. · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210000, China.

  4. GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment

    Aug 27, 2026Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng +2Anomaly DetectionIndustrial Anomaly Detection

    National Taiwan University Taipei, Taiwan · National Taiwan Normal University Taipei, Taiwan · Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA +3

  5. Stagewise Anomaly Detection for E-Transaxle Quality Monitoring Using Wavelet and STFT Features

    Aug 27, 2026Mohammad N. Bisheh, Rajesh Gupta, Qian Wang +5Anomaly DetectionIndustrial Anomaly Detection

    H. Milton Stewart School of Industrial & Systems Engineering Georgia Institute of Technology Atlanta, USA · Ford Motor Company USA · Amazon Web Services

  6. See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection

    Aug 25, 2026Shang-Fu Chen, Kuan-Chuan Peng, Jhih-Ciang Wu +2Multi-View Feature FusionAnomaly Detection

    National Taiwan University Taipei, Taiwan · Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA · National Taiwan Normal University Taipei, Taiwan +2

  7. DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection

    Aug 24, 2026Wenyang Liu, Tianyi Liu, Dongshuo Zhang +2Few-Shot Anomaly DetectionIndustrial Anomaly Detection

    Nanyang Technological University Singapore · Huazhong University of Science and Technology, Wuhan, China

  8. Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination

    Aug 24, 2026Mustafa Umut Ozbek, Taiwo Ojo, Pooria Madani +2Industrial Control System SecurityAnomaly Detection

    Ontario Tech University, Oshawa, Ontario, Canada

  9. What Remains Normal? Clean Images Miss Useful Near-Defect Normal Patches for Anomaly Detection

    Aug 24, 2026Joongwon Chae, Runming Wang, Peiwu QinIndustrial Anomaly DetectionReconstruction-Based Anomaly Detection

    Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · RatelSoft · Guangdong Provincial Laboratory of Traditional Chinese Medicine, Hengqin, Guangdong, China

  10. Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Study

    Aug 24, 2026Guan-Hua Wen, Kuan-Yu ChenTime-Series Foundation ModelsTime-Series Anomaly Detection

    Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei, Taiwan

  11. Multi-Modal Anomaly Detection: A Survey

    Aug 24, 2026Xudong Mou, Zexin Wu, Chuan Luo +4Multimodal Anomaly DetectionIndustrial Anomaly Detection

    School of Computer Science and Engineering, Beihang University, Beijing 100191, China · School of Software, Beihang University, Beijing 100191, China · Shandong Inspur Intelligent Production Technology Co., Ltd, Jinan, 250101, China

  12. When More References Hurt: Contamination-Aware DINOv2 Memory Banks for Few-Shot Steel Defect Detection

    Aug 22, 2026Hannaneh Kalantary, Javad KhoramdelIndustrial Anomaly DetectionAnomaly Detection

    University of Padua · University of Vienna

  13. Trustworthy Visual Quality Inspection under Data Scarcity in Manufacturing

    Aug 22, 2026Panagiotis Sapoutzoglou, Jessy Ribaira, Martin Kanounnikoff +3Industrial InspectionIndustrial Anomaly Detection

    National Technical University of Athens, Athens, Greece · German Aerospace Center (DLR), Germany · Philips, High Tech Campus 52, 5656 AG Eindhoven, The Netherlands +1

  14. CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation

    Aug 19, 2026Seungjun Chu, Seokhee Han, Mateusz Nowak +1Industrial Anomaly DetectionAnomaly Localization

    Korea University · Dartmouth College

  15. SPARC: Subspace Position-Aware Robust Few-Shot Calibration for Distribution-Shifted Industrial Anomaly Detection

    Aug 19, 2026Seokhee Han, Seungjun Chu, Mateusz Nowak +1Industrial Anomaly DetectionAnomaly Detection

    Korea University

  16. Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring

    Aug 18, 2026Steven C. Nesbit, Victor M. Vergara, Michael A. Felix +4Anomaly DetectionIndustrial Anomaly Detection

    Information Sciences, CAI-3, Los Alamos National Laboratory, Los Alamos, USA · Information Sciences (CAI-3), Los Alamos National Laboratory, Los Alamos, NM, USA · AeroVironment Inc., Albuquerque, USA +7

  17. Training-Free Human-in-the-Loop Anomaly Detection via Memory Bank Correction

    Aug 18, 2026Ayusha Abbas, Saram Abbas, Kabita AdhikariAnomaly DetectionIndustrial Anomaly Detection

    School of Engineering, Newcastle University, Newcastle upon Tyne, UK

  18. Continuity-Driven Representation Learning for Industrial Defect Detection

    Aug 18, 2026Minjong Kim, Hyun Jun Kim, Jeongrae Kim +2Object DetectionIndustrial Anomaly Detection

    Chung-Ang University Seoul, Republic of Korea

  19. Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies

    Aug 17, 2026Konstantinos E. Kampourakis, Vasileios Gkioulos, Sokratis KatsikasCPS SecurityCybersecurity

    Norwegian University of Science and Technology (NTNU), 2802 Gjøvik, Norway

  20. Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection

    Aug 15, 2026Yoon Gyo Jung, Jaewoo Park, Kuan-Chuan Peng +2Continual Anomaly DetectionAnomaly Detection

    Electrical and Computer Engineering Department Northeastern University Boston, MA, USA · AIVEX Inc. Seoul, South Korea · Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA

  21. Distribution-free false-alarm calibration and chance-corrected spatial evaluation for industrial anomaly detection

    Aug 15, 2026Jie DengIndustrial Anomaly DetectionAnomaly Detection

    Tongji University

  22. Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis

    Aug 14, 2026Yicheng Kang, Yuling Jiao, Xin Geng +1Fault DiagnosisIndustrial Inspection

    Farmer School of Business, Miami University, OH. USA · School of Mathematics & Statistics, Wuhan University, Hubei, China · Miami Herbert Business School, University of Miami, FL. USA +1

  23. Low Cost Two-Stage Fabric Defect Detection at the Edge

    Aug 12, 2026Rasel Hossen, Diptajoy Mistry, Mosaddek Hossain KamalIndustrial Anomaly DetectionIndustrial Inspection

    Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh

  24. Towards an approach to multivariate outlier detection for District Heating System data

    Aug 11, 2026Rajko Turudija, Dušan Stojiljković, Milan Zdravković +1Isolation ForestIndustrial Anomaly Detection

    Faculty of Mechanical Engineering University of Niš ul. Aleksandra Medvedeva 14, 18000 Niš, Serbia

  25. ADOPD: Reference-Privileged On-Policy Distillation for MLLM-Based Industrial Anomaly Detection

    Aug 10, 2026Jingtai He, Shiyuan Meng, Wenchao Meng +1Industrial Anomaly DetectionMultimodal Large Language Models

    Zhejiang University

  26. An Explainable GNN Framework for Component-Level Anomaly Diagnosis

    Aug 10, 2026Sena Ozgunay, Louise Trav{é}-Massuy{è}s, Jean-Michel Loubes +1Industrial Anomaly DetectionRoot Cause Analysis

    Comue de Toulouse, LAAS-DISCO, IMT · Comue de Toulouse, LAAS-DISCO · REGALIA, IMT +1

  27. LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturing

    Aug 8, 2026Wenbo Sui, Daniel Lichau, Harold Phelippeau +1Industrial Anomaly DetectionMultimodal Anomaly Detection

    Thermo Fisher Scientific, 39 Rue d’Armagnac, Bordeaux, 33800, France · DTU Energy, Technical University of Denmark, Fysikvej 310, Kgs. Lyngby, 2800, Denmark · Thermo Fisher Scientific, 5350 NE Dawson Creek Dr, Hillsboro, OR 97124, USA

  28. DeCo: Zero-Shot Industrial Anomaly Generation through Decoupling and Recoupling

    Aug 8, 2026Shilei Zeng, Xurui Li, Yaohan Tang +1Industrial Anomaly DetectionSynthetic Anomaly Generation

    School of Electronic Information and Communications, Huazhong University of Science and Technology

  29. UniScale: Arbitrary-Scale Industrial Anomaly Generation

    Aug 8, 2026Shilei Zeng, Linxin Guan, Xurui Li +2Industrial Anomaly DetectionSynthetic Anomaly Generation

    School of Electronic Information and Communications, Huazhong University of Science and Technology

  30. From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection

    Aug 7, 2026Mike Szklarzewski, CJ George, Gavin Smithson +10Human-in-the-Loop AnnotationIndustrial Anomaly Detection

    Department of Computing Sciences, Coastal Carolina University, Conway, SC, USA · High Performance Computing Design, Los Alamos National Laboratory, Los Alamos, NM, USA · Academy of Arts Science and Technology, Horry County School District, Myrtle Beach, SC, USA +3