Deep Industrial Image Anomaly Detection: A Survey
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Jiaqi, Xie, Guoyang, Wang, Jinbao, Li, Shangnian, Wang, Chengjie, Zheng, Feng, Jin, Yaochu |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
by: Xie, Guoyang, et al.
Published: (2023)
by: Xie, Guoyang, et al.
Published: (2023)
ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection
by: Zhang, Qunyi, et al.
Published: (2025)
by: Zhang, Qunyi, et al.
Published: (2025)
A Survey on Industrial Anomalies Synthesis
by: Wang, Yanshu, et al.
Published: (2025)
by: Wang, Yanshu, et al.
Published: (2025)
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
by: Xie, Guoyang, et al.
Published: (2023)
by: Xie, Guoyang, et al.
Published: (2023)
STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment
by: Xu, Xichen, et al.
Published: (2025)
by: Xu, Xichen, et al.
Published: (2025)
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
by: Liu, Jiaqi, et al.
Published: (2024)
by: Liu, Jiaqi, et al.
Published: (2024)
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning
by: Zhu, Hongze, et al.
Published: (2024)
by: Zhu, Hongze, et al.
Published: (2024)
Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective
by: Long, Kaifang, et al.
Published: (2024)
by: Long, Kaifang, et al.
Published: (2024)
Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspective
by: Long, Kaifang, et al.
Published: (2026)
by: Long, Kaifang, et al.
Published: (2026)
FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis
by: Xu, Xichen, et al.
Published: (2025)
by: Xu, Xichen, et al.
Published: (2025)
Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection
by: Liang, Hanzhe, et al.
Published: (2024)
by: Liang, Hanzhe, et al.
Published: (2024)
IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection
by: Guo, Bingyang, et al.
Published: (2025)
by: Guo, Bingyang, et al.
Published: (2025)
MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
by: Jiang, Xi, et al.
Published: (2024)
by: Jiang, Xi, et al.
Published: (2024)
Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection
by: Tu, Yuanpeng, et al.
Published: (2024)
by: Tu, Yuanpeng, et al.
Published: (2024)
Collaborative Reconstruction and Repair for Multi-class Industrial Anomaly Detection
by: Wang, Qishan, et al.
Published: (2025)
by: Wang, Qishan, et al.
Published: (2025)
AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison
by: Jiang, Xi, et al.
Published: (2026)
by: Jiang, Xi, et al.
Published: (2026)
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection
by: Lin, Yuxuan, et al.
Published: (2024)
by: Lin, Yuxuan, et al.
Published: (2024)
Learning Multi-view Anomaly Detection with Efficient Adaptive Selection
by: He, Haoyang, et al.
Published: (2024)
by: He, Haoyang, et al.
Published: (2024)
FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection
by: Liu, Tongkun, et al.
Published: (2023)
by: Liu, Tongkun, et al.
Published: (2023)
One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection
by: Gao, Bin-Bin, et al.
Published: (2026)
by: Gao, Bin-Bin, et al.
Published: (2026)
SARD: Segmentation-Aware Anomaly Synthesis via Region-Constrained Diffusion with Discriminative Mask Guidance
by: Wang, Yanshu, et al.
Published: (2025)
by: Wang, Yanshu, et al.
Published: (2025)
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
by: Jiang, Xi, et al.
Published: (2024)
by: Jiang, Xi, et al.
Published: (2024)
Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection
by: Sun, Yihan, et al.
Published: (2026)
by: Sun, Yihan, et al.
Published: (2026)
Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision
by: Jin, Yizhou, et al.
Published: (2026)
by: Jin, Yizhou, et al.
Published: (2026)
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework
by: Cheng, Yuqi, et al.
Published: (2024)
by: Cheng, Yuqi, et al.
Published: (2024)
Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset
by: Liu, Chuni, et al.
Published: (2025)
by: Liu, Chuni, et al.
Published: (2025)
CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection
by: Bai, Haoyu, et al.
Published: (2025)
by: Bai, Haoyu, et al.
Published: (2025)
AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion
by: Hu, Jie, et al.
Published: (2024)
by: Hu, Jie, et al.
Published: (2024)
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising
by: Wang, Chengjie, et al.
Published: (2024)
by: Wang, Chengjie, et al.
Published: (2024)
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
by: Hu, Teng, et al.
Published: (2023)
by: Hu, Teng, et al.
Published: (2023)
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
by: Wang, Chengjie, et al.
Published: (2024)
by: Wang, Chengjie, et al.
Published: (2024)
Multimodal Industrial Anomaly Detection via Geometric Prior
by: Li, Min, et al.
Published: (2026)
by: Li, Min, et al.
Published: (2026)
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
by: Huang, Ziming, et al.
Published: (2024)
by: Huang, Ziming, et al.
Published: (2024)
Text-Guided Multimodal Unified Industrial Anomaly Detection
by: Li, Zewen, et al.
Published: (2026)
by: Li, Zewen, et al.
Published: (2026)
A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection
by: Zhang, Jiangning, et al.
Published: (2024)
by: Zhang, Jiangning, et al.
Published: (2024)
A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features
by: Liang, Hanzhe, et al.
Published: (2025)
by: Liang, Hanzhe, et al.
Published: (2025)
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection
by: Liang, Hanzhe, et al.
Published: (2025)
by: Liang, Hanzhe, et al.
Published: (2025)
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025)
by: Tong, Xuan, et al.
Published: (2025)
Towards High-Resolution Industrial Image Anomaly Detection
by: Zhang, Ximiao, et al.
Published: (2025)
by: Zhang, Ximiao, et al.
Published: (2025)
Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era
by: Zhu, Wenbing, et al.
Published: (2025)
by: Zhu, Wenbing, et al.
Published: (2025)
Similar Items
-
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
by: Xie, Guoyang, et al.
Published: (2023) -
ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection
by: Zhang, Qunyi, et al.
Published: (2025) -
A Survey on Industrial Anomalies Synthesis
by: Wang, Yanshu, et al.
Published: (2025) -
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
by: Xie, Guoyang, et al.
Published: (2023) -
STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment
by: Xu, Xichen, et al.
Published: (2025)