Awesome Industrial Anomaly Detection

We discuss public datasets and related studies in detail. Welcome to read our paper and make comments.

[中文版]我们详细分析了相关研究和公开数据集。欢迎阅读我们的论文并提出意见。

Deep Industrial Image Anomaly Detection: A Survey (Accepted by Machine Intelligence Research[CiteScore 8.4 Q1])

We will keep focusing on this field and update relevant information.

(Keywords: anomaly detection, anomaly segmentation, industrial image, defect detection)

SOTA methods with code

Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection [CVPR 2023][code]
SimpleNet: A Simple Network for Image Anomaly Detection and Localization [CVPR 2023][code]
Multimodal Industrial Anomaly Detection via Hybrid Fusion [CVPR 2023][code]
Revisiting Reverse Distillation for Anomaly Detection [CVPR 2023] [code]
Towards total recall in industrial anomaly detection [CVPR 2022][code]
DSR: A dual subspace re-projection network for surface anomaly detection [ECCV 2022][code]
A Unified Model for Multi-class Anomaly Detection [NIPS 2022][code]

Recent research

CVPR 2023

Workshop on Vision-Based Industrial Inspection [CVPR Workshop paper list 2023]
Visual Anomaly and Novelty Detection [CVPR Workshop paper list 2023]
Revisiting Reverse Distillation for Anomaly Detection [CVPR 2023] [code]
OmniAL A unifiled CNN framework for unsupervised anomaly localization [CVPR 2023]
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection [CVPR 2023][code]
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection [CVPR 2023]
Diversity-Measurable Anomaly Detection [CVPR 2023]
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [CVPR 2023]
SimpleNet: A Simple Network for Image Anomaly Detection and Localization [CVPR 2023][code]
PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow [CVPR 2023]
Multimodal Industrial Anomaly Detection via Hybrid Fusion [CVPR 2023][code]
Prototypical Residual Networks for Anomaly Detection and Localization [CVPR 2023]
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [CVPR 2023]

SAM segment anything

Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications [2023 SAM tech report]
SAM Struggles in Concealed Scenes -- Empirical Study on "Segment Anything" [2023 SAM tech report]
Segment Any Anomaly without Training via Hybrid Prompt Regularization [2023] [code]
Application of Segment Anything Model for Civil Infrastructure Defect Assessment [2023 SAM tech report]

ICLR 2023

Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore [ICLR 2023]
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection [ICLR 2023]

Others

VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON [2023][data]
Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer [2023]
ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection [2023]
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision [2023]
Zero-Shot Batch-Level Anomaly Detection [2023]
Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection [WACV 2023]
Anomaly Detection with Conditioned Denoising Diffusion Models [2023]
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification [2023]
What makes a good data augmentation for few-shot unsupervised image anomaly detection? [CVPRW 2023]
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection [2023][code]
Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction [2023] [code]
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies [2023]
Set Features for Fine-grained Anomaly Detection [2023][code]
DiffusionAD: Denoising Diffusion for Anomaly Detection [2023]
SSGD: A smartphone screen glass dataset for defect detection [2023] [dataset is coming soon]
Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization [TII 2023] [code]
CVPR 1st workshop on Vision-based InduStrial InspectiON [CVPR 2023 Workshop] [data link]
VAND: Visual Anomaly and Novelty Detection [CVPR 2023 Workshop]

Paper Tree (Classification of representative methods)

Timeline

Paper list for industrial image anomaly detection

Related Survey, Benchmark and Framework

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure [2015]
Visual-based defect detection and classification approaches for industrial applications: a survey [2020]
Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey [TIM 2022]
A Survey on Unsupervised Industrial Anomaly Detection Algorithms [2022]
Benchmarking Unsupervised Anomaly Detection and Localization [2022]
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [2023]
A Deep Learning-based Software for Manufacturing Defect Inspection [TII 2017][code]
Anomalib: A Deep Learning Library for Anomaly Detection [code]
Ph.D. thesis of Paul Bergmann(The first author of MVTec AD series) [2022]

2 Unsupervised AD

2.1 Feature-Embedding-based Methods

2.1.1 Teacher-Student

Revisiting Reverse Distillation for Anomaly Detection [CVPR 2023] [code]
Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings [CVPR 2020]
Multiresolution knowledge distillation for anomaly detection [CVPR 2021]
Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison [CVPR 2021]
Reconstruction Student with Attention for Student-Teacher Pyramid Matching [2021]
Student-Teacher Feature Pyramid Matching for Anomaly Detection [2021][code]
PFM and PEFM for Image Anomaly Detection and Segmentation [CASE 2022] [TII 2022][code]
Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection [2022]
Anomaly Detection via Reverse Distillation from One-Class Embedding [CVPR 2022][code]
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection [WACV 2022][code]
Informative knowledge distillation for image anomaly segmentation [2022][code]

2.1.2 One-Class Classification (OCC)

Patch svdd: Patch-level svdd for anomaly detection and segmentation [ACCV 2020]
Anomaly detection using improved deep SVDD model with data structure preservation [2021]
A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection [2021]
MOCCA: Multilayer One-Class Classification for Anomaly Detection [2021]
Defect Detection of Metal Nuts Applying Convolutional Neural Networks [2021]
Panda: Adapting pretrained features for anomaly detection and segmentation [2021]
Mean-shifted contrastive loss for anomaly detection [2021]
Learning and Evaluating Representations for Deep One-Class Classification [2020]
Self-supervised learning for anomaly detection with dynamic local augmentation [2021]
Contrastive Predictive Coding for Anomaly Detection [2021]
Cutpaste: Self-supervised learning for anomaly detection and localization [ICCV 2021][unofficial code]
Consistent estimation of the max-flow problem: Towards unsupervised image segmentation [2020]
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities [2022][unofficial code]
SimpleNet: A Simple Network for Image Anomaly Detection and Localization [CVPR 2023][code]

2.1.3 Distribution-Map

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity [Sensors 2018]
A Multi-Scale A Contrario method for Unsupervised Image Anomaly Detection [2021]
Modeling the distribution of normal data in pre-trained deep features for anomaly detection [2021]
Transfer Learning Gaussian Anomaly Detection by Fine-Tuning Representations [2021]
PEDENet: Image anomaly localization via patch embedding and density estimation [2022]
Unsupervised image anomaly detection and segmentation based on pre-trained feature mapping [2022]
Position Encoding Enhanced Feature Mapping for Image Anomaly Detection [2022][code]
Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization [ICME 2022]
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework [2021][code]
Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows [2021][unofficial code]
Same same but differnet: Semi-supervised defect detection with normalizing flows [WACV 2021][code]
Fully convolutional cross-scale-flows for image-based defect detection [WACV 2022][code]
Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows [WACV 2022][code]
CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks [2022]
AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection [2022]
Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization [TII 2023][code]
PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow [CVPR 2023]

2.1.4 Memory Bank

ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection [2023]

Sub-image anomaly detection with deep pyramid correspondences [2020]
Semi-orthogonal embedding for efficient unsupervised anomaly segmentation [2021]
Anomaly Detection Via Self-Organizing Map [2021]
PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization [ICPR 2021][unofficial code]
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature [2021]
Towards total recall in industrial anomaly detection[CVPR 2022][code]
CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization[2022][code]
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection[2022]
N-pad: Neighboring Pixel-based Industrial Anomaly Detection [2022]
Image Anomaly Detection and Localization with Position and Neighborhood Information [2022]
Multi-scale patch-based representation learning for image anomaly detection and segmentation [2022]
SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation [ECCV 2022]
Diversity-Measurable Anomaly Detection [CVPR 2023]

2.2 Reconstruction-Based Methods

2.2.1 Autoencoder (AE)

Improving unsupervised defect segmentation by applying structural similarity to autoencoders [2018]
Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model [Sensors 2018]
An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces [TIM 2018]
Unsupervised anomaly detection using style distillation [2020]
Unsupervised two-stage anomaly detection [2021]
Dfr: Deep feature reconstruction for unsupervised anomaly segmentation [Neurocomputing 2020]
Unsupervised anomaly segmentation via multilevel image reconstruction and adaptive attention-level transition [2021]
Encoding structure-texture relation with p-net for anomaly detection in retinal images [2020]
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with stain-shaped noise [2021]
Unsupervised anomaly detection for surface defects with dual-siamese network [2022]
Divide-and-assemble: Learning block-wise memory for unsupervised anomaly detection [ICCV 2021]
Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection [2022][code]
Spatial Contrastive Learning for Anomaly Detection and Localization [2022]
Superpixel masking and inpainting for self-supervised anomaly detection [BMVC 2020]
Iterative image inpainting with structural similarity mask for anomaly detection [2020]
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization [2022]
Reconstruction by inpainting for visual anomaly detection [PR 2021]
Draem-a discriminatively trained reconstruction embedding for surface anomaly detection [ICCV 2021][code]
DSR: A dual subspace re-projection network for surface anomaly detection [ECCV 2022][code]
Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization [ECCV 2022][code]
Self-Supervised Training with Autoencoders for Visual Anomaly Detection [2022]
Self-supervised predictive convolutional attentive block for anomaly detection [CVPR 2022 oral][code]
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [TPAMI 2022][code]
Iterative energy-based projection on a normal data manifold for anomaly localization [2019]
Towards visually explaining variational autoencoders [2020]
Deep generative model using unregularized score for anomaly detection with heterogeneous complexity [2020]
Anomaly localization by modeling perceptual features [2020]
Image anomaly detection using normal data only by latent space resampling [2020]

2.2.2 Generative Adversarial Networks (GANs)

Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection [2022][code]
Learning semantic context from normal samples for unsupervised anomaly detection [AAAI 2021]
Anoseg: Anomaly segmentation network using self-supervised learning [2021]
A Surface Defect Detection Method Based on Positive Samples [PRICAI 2018]

2.2.3 Transformer

VT-ADL: A vision transformer network for image anomaly detection and localization [ISIE 2021]
ADTR: Anomaly Detection Transformer with Feature Reconstruction [2022]
AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder [2022]
HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization [2022]
Inpainting transformer for anomaly detection [ICIAP 2022]
Masked Swin Transformer Unet for Industrial Anomaly Detection [2022]
Masked Transformer for image Anomaly Localization [TII 2022]

2.2.4 Diffusion Model

Anomaly Detection with Conditioned Denoising Diffusion Models [2023]
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise [CVPR Workshop 2022]
Unsupervised Visual Defect Detection with Score-Based Generative Model[2022]
DiffusionAD: Denoising Diffusion for Anomaly Detection [2023]

2.3 Supervised AD

More Normal samples With (Less Abnormal Samples or Weak Labels)

Neural batch sampling with reinforcement learning for semi-supervised anomaly detection [ECCV 2020]
Explainable Deep One-Class Classification [ICLR 2020]
Attention guided anomaly localization in images [ECCV 2020]
Mixed supervision for surface-defect detection: From weakly to fully supervised learning [2021]
Explainable deep few-shot anomaly detection with deviation networks [2021]
Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [CVPR 2022]
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types[WACV 2023]
Prototypical Residual Networks for Anomaly Detection and Localization [CVPR 2023]
Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer [2023]

More Abnormal Samples

Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection [2022]
An effective framework of automated visual surface defect detection for metal parts [2021]
Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification [TIP 2021]
Reference-based defect detection network [TIP 2021]
Fabric defect detection using tactile information [ICRA 2021]
A lightweight spatial and temporal multi-feature fusion network for defect detection [TIP 2020]
SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection [Robotics and Computer-Integrated Manufacturing 2020]
A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection [IEEE Access 2019]
SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection [Applied Sciences 2019]
Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types [CACIE 2018]
Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning [2018]
Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks [Applied Sciences 2018]
Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network [IFAC-PapersOnLine 2018]
Domain adaptation for automatic OLED panel defect detection using adaptive support vector data description [IJCV 2017]
Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network [TIM 2017]
Deep Active Learning for Civil Infrastructure Defect Detection and Classification Computing in civil engineering 2017
A fast and robust convolutional neural network-based defect detection model in product quality control [IJAMT 2017]
Defects Detection Based on Deep Learning and Transfer Learning [Metallurgical & Mining Industry 2015]
Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection [CIRP annals 2016]

3 Other Research Direction

3.1 Few-Shot AD

Learning unsupervised metaformer for anomaly detection [ICCV 2021]
Registration based few-shot anomaly detection [ECCV 2022 oral][code]
Same same but differnet: Semi-supervised defect detection with normalizing flows [(Distribution)WACV 2021]
Towards total recall in industrial anomaly detection [(Memory bank)CVPR 2022]
A hierarchical transformation-discriminating generative model for few shot anomaly detection [ICCV 2021]
Anomaly detection of defect using energy of point pattern features within random finite set framework [2021]

Zero-Shot AD

Zero-Shot Batch-Level Anomaly Detection [2023]
Zero-shot versus Many-shot: Unsupervised Texture Anomaly Detection [WACV 2023]
MAEDAY: MAE for few and zero shot AnomalY-Detection [2022]
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation [CVPR 2023]
Segment Any Anomaly without Training via Hybrid Prompt Regularization [2023] [code]

3.2 Noisy AD

Trustmae: A noise-resilient defect classification framework using memory-augmented auto-encoders with trust regions [WACV 2021]
Self-Supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection [TMLR 2021]
Data refinement for fully unsupervised visual inspection using pre-trained networks [2022]
Latent Outlier Exposure for Anomaly Detection with Contaminated Data [ICML 2022]
Deep one-class classification via interpolated gaussian descriptor [AAAI 2022 oral][code]
SoftPatch: Unsupervised Anomaly Detection with Noisy Data [NeurIPS 2022])[code is coming soon]

3.3 Anomaly Synthetic

Cutpaste: Self-supervised learning for anomaly detection and localization [(OCC)ICCV 2021][unofficial code]
Draem-a discriminatively trained reconstruction embedding for surface anomaly detection [(Reconstruction AE)ICCV 2021][code]
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities [(OCC)2022][unofficial code]
A High-Efficiency Fully Convolutional Networks for Pixel-Wise Surface Defect Detection [IEEE Access 2019]
Multistage GAN for fabric defect detection [2019]
Gan-based defect synthesis for anomaly detection in fabrics [2020]
Defect image sample generation with GAN for improving defect recognition [2020]
Defective samples simulation through neural style transfer for automatic surface defect segment [2020]
A simulation-based few samples learning method for surface defect segmentation [2020]
Synthetic data augmentation for surface defect detection and classification using deep learning [2020]
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation [BMVC 2022]
Defect-GAN: High-fidelity defect synthesis for automated defect inspectio [2021]
EID-GAN: Generative Adversarial Nets for Extremely Imbalanced Data Augmentation[TII 2022]

3.4 3D AD

Anomaly detection in 3d point clouds using deep geometric descriptors [WACV 2022]
Back to the feature: classical 3d features are (almost) all you need for 3D anomaly detection [2022][code]
Anomaly Detection Requires Better Representations [2022]
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection [WACV 2022]
Multimodal Industrial Anomaly Detection via Hybrid Fusion [CVPR 2023]
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection [2023][code]

3.5 Continual AD

Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision [2023]
Towards Continual Adaptation in Industrial Anomaly Detection [ACM MM 2022]

3.6 Uniform AD

A Unified Model for Multi-class Anomaly Detection [NIPS 2022] [code]
OmniAL A unifiled CNN framework for unsupervised anomaly localization [CVPR 2023]

3.7 Logical AD

Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [IJCV 2022]
Set Features for Fine-grained Anomaly Detection[2023] [code]
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification [2023]
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies [2023]

4 Dataset

(NEU surface defect dataset)A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects [2013] [data]
(Steel tube dataset)Deep learning based steel pipe weld defect detection [2021] [data]
(Steel defect dataset)Severstal: Steel Defect Detection [data 2019]
(NanoTwice)Defect detection in SEM images of nanofibrous materials [TII 2016] [data]
(GDXray)GDXray: The database of X-ray images for nondestructive testing [2015] [data]
(DEEP PCB)Online PCB defect detector on a new PCB defect dataset [2019] [data]
(Fabric dataset)Fabric inspection based on the Elo rating method [PR 2016]
(KolektorSDD)Segmentation-based deep-learning approach for surface-defect detection [Journal of Intelligent Manufacturing] [data]
(KolektorSDD2)Mixed supervision for surface-defect detection: From weakly to fully supervised learning [Computers in Industry 2021] [data]
(RSDD)A hierarchical extractor-based visual rail surface inspection system [2017]
(Eyecandies)The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization [ACCV 2022] [data]
(MVTec AD)MVTec AD: A comprehensive real-world dataset for unsupervised anomaly detection [CVPR 2019] [IJCV 2021] [data]
(MVTec 3D-AD)The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization [VISAPP 2021] [data]
(MVTec LOCO-AD)Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [IJCV 2022] [data]
(MPDD)Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions [ICUMT 2021] [data]
(BTAD)VT-ADL: A vision transformer network for image anomaly detection and localization [2021] [data]
(VisA)SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation [ECCV 2022] [data]
(MTD)Surface defect saliency of magnetic tile [2020] [data]
(DAGM)DAGM dataset [data 2007]
(MIAD)Miad:A maintenance inspection dataset for unsupervised anomaly detection [2022] [data]
CVPR 1st workshop on Vision-based InduStrial InspectiON [homepage] [data]
(SSGD)SSGD: A smartphone screen glass dataset for defect detection [2023][dataset is coming soon]
(AeBAD)Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction [2023] [data]
VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON [2023] [data]

BibTex Citation

If you find this paper and repository useful, please cite our paper☺️.

@article{liu2023deep,
  title={Deep Industrial Image Anomaly Detection: A Survey},
  author={Liu, Jiaqi and Xie, Guoyang and Wang, Jingbao and Li, Shangnian and Wang, Chengjie and Zheng, Feng and Jin, Yaochu},
  journal={arXiv e-prints},
  pages={arXiv--2301},
  year={2023}
}

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