mlsanityData to 14 Oct 2026

MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection

arXiv 2211.13968 ↗First posted 25 Nov 2022cs.CVcited by 19

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Abstract (arXiv)

Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to keep equipment in optimum working condition particularly outdoors. Due to the scarcity of the defective samples, unsupervised anomaly detection has attracted great attention in recent years. However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working. We focus on outdoor maintenance inspection and contribute a comprehensive Maintenance Inspection Anomaly Detection (MIAD) dataset which contains more than 100K high-resolution color images in various outdoor industrial scenarios. This dataset is generated by a 3D graphics software and covers both surface and logical anomalies with pixel-precise ground truth. Extensive evaluations of representative algorithms for unsupervised anomaly detection are conducted, and we expect MIAD and corresponding experimental results can inspire research community in outdoor unsupervised anomaly detection tasks. Worthwhile and related future work can be spawned from our new dataset.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 26 of its 48 references that are in the map. First posted on arXiv 25 Nov 2022.

Builds on

26 of 48 references.

References · 26

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  11. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  12. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
14 more
  1. 2020Sub-Image Anomaly Detection with Deep Pyramid Correspondencescited by 300
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Citing papers per year: 2023: 4; 2024: 3; 2025: 7; 2026: 5.

19 papers.

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  1. 2023Deep Industrial Image Anomaly Detection: A Surveycited by 148
  2. 2023Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detectioncited by 21
  3. 2024VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detectioncited by 13
  4. 2023Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentationcited by 6
  5. 2024Variational Autoencoder for Anomaly Detection: A Comparative Studycited by 6
  6. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  7. 2023Scalable Concept Extraction in Industry 4.0cited by 2
  8. 2024SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspectioncited by 2
  9. 2025ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defectscited by 2
  10. 2025ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categoriescited by 2
  11. 2025Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detectioncited by 1
  12. 2026AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Modelscited by 1
7 more
  1. 2026MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Modelscited by 1
  2. 2026VD-DeepStack: Bridging Visual Comparison and Language Reasoning for Few-Shot Anomaly Detectioncited by 1
  3. 2025Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generationcited by 0
  4. 2025CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detectioncited by 0
  5. 2025Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environmentscited by 0
  6. 2026DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Modelscited by 0
  7. 2026Clean-Reference Streaming Detection of Lens Occlusion and Photometric Transitions for Camera Tamper Monitoringcited by 0