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Deep Industrial Image Anomaly Detection: A Survey
We will keep focusing on this field and update relevant information.
(Keywords: anomaly detection, anomaly segmentation, industrial image, defect detection.)
- DiffusionAD: Denoising Diffusion for Anomaly Detection [2023]
- SSGD: A smartphone screen glass dataset for defect detection [2023][dataset is coming soon]
- In-painting Radiography Images for Unsupervised Anomaly Detection [CVPR 2023]
- Diversity-Measurable Anomaly Detection [CVPR 2023]
- PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow [CVPR 2023]
- Multimodal Industrial Anomaly Detection via Hybrid Fusion [CVPR 2023][code is coming soon]
- 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]
- Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization [TII 2023][code]
- CVPR 1st workshop on Vision-based InduStrial InspectiON[homepage][data link]
- A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure [2015]
- (czimmermann2020visual)Visual-based defect detection and classification approaches for industrial applications: a survey [2020]
- (tao2022deep)Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey [TIM 2022]
- (cui2022survey)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]
- Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings [CVPR 2020]
- Student-Teacher Feature Pyramid Matching for Anomaly Detection [2021]
- Multiresolution knowledge distillation for anomaly detection [CVPR 2021]
- Reconstruction Student with Attention for Student-Teacher Pyramid Matching [2021]
- 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]
- 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]
- 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]
- 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]
- Improving unsupervised defect segmentation by applying structural similarity to autoencoders [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]
- In-painting Radiography Images for Unsupervised Anomaly Detection [CVPR 2023]
- Learning semantic context from normal samples for unsupervised anomaly detection [AAAI 2021]
- Anoseg: Anomaly segmentation network using self-supervised learning [2021]
- Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection [2022][code]
- 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]
- Denoising diffusion probabilistic models [2020]
- 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]
- 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]
- Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection [2022]
- Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [CVPR 2022]
- Domain adaptation for automatic OLED panel defect detection using adaptive support vector data description [IJCV 2017]
- 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]
- Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning [2018]
- 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]
- MAEDAY: MAE for few and zero shot AnomalY-Detection [2022]
- 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 [NIPS 2020][code is coming soon]
- 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]
- 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]
- 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]
- Towards Continual Adaptation in Industrial Anomaly Detection [ACM MM 2022]
- A Unified Model for Multi-class Anomaly Detection [NIPS 2022]
- Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [IJCV 2022]
- A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects [2013]
- Deep learning based steel pipe weld defect detection [2021]
- (SDD)Severstal: Steel Defect Detection [2019]
- (carrera2016defect)Defect detection in SEM images of nanofibrous materials [2016]
- (GDXray)GDXray: The database of X-ray images for nondestructive testing [2015]
- Online PCB defect detector on a new PCB defect dataset [2019]
- Fabric inspection based on the Elo rating method [2016]
- (KolektorSDD)Segmentation-based deep-learning approach for surface-defect detection [Journal of Intelligent Manufacturing]
- (KolektorSDD2)Mixed supervision for surface-defect detection: From weakly to fully supervised learning [Computers in Industry 2021]
- (RSDD)A hierarchical extractor-based visual rail surface inspection system [2017]
- (Eyecandies)The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization [ACCV 2022]
- (MVTec AD)MVTec AD: A comprehensive real-world dataset for unsupervised anomaly detection [CVPR 2019]
- (MVTec 3D-AD)The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization [VISAPP 2021]
- (MVTec LOCO-AD)Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization [IJCV 2022]
- (MPDD)Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions [2021]
- (BTAD)VT-ADL: A vision transformer network for image anomaly detection and localization [2021]
- (VisA)SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation [ECCV 2022]
- (MTD)Surface defect saliency of magnetic tile [2020]
- (DAGM)DAGM dataset [2007]
- CVPR 1st workshop on Vision-based InduStrial InspectiON[homepage][data link]
- SSGD: A smartphone screen glass dataset for defect detection [2023][dataset is coming soon]
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}
}