Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection

Deep Residual Learning for Image Recognition

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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

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Deep Industrial Image Anomaly Detection: A Survey

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AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model

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Diverse Image Inpainting with Bidirectional and Autoregressive Transformers

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Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation

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Region- and Strength-Controllable GAN for Defect Generation and Segmentation in Industrial Images

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Datasets and processing methods for boosting visual inspection of civil infrastructure: A comprehensive review and algorithm comparison for crack classification, segmentation, and detection

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A New Contrastive GAN With Data Augmentation for Surface Defect Recognition Under Limited Data

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DLS -GAN: Generative Adversarial Nets for Defect Location Sensitive Data Augmentation

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Mask2Defect: A Prior Knowledge-Based Data Augmentation Method for Metal Surface Defect Inspection

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Industrial UAV-Based Unsupervised Domain Adaptive Crack Recognitions: From Database Towards Real-Site Infrastructural Inspections

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Unbalanced Feature Transport for Exemplar-based Image Translation

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A Survey of Defect Detection Applications Based on Generative Adversarial Networks

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DiffDD: A surface defect detection framework with diffusion probabilistic model

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DG-GAN: A High Quality Defect Image Generation Method for Defect Detection

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Computer vision defect detection on unseen backgrounds for manufacturing inspection

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Survey on industrial defect detection with deep learning

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A New Foreground-Perception Cycle-Consistent Adversarial Network for Surface Defect Detection With Limited High-Noise Samples

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An Overview of Image Generation of Industrial Surface Defects

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Erasing-inpainting-based data augmentation using denoising diffusion probabilistic models with limited samples for generalized surface defect inspection

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Diversifying Tire-Defect Image Generation Based on Generative Adversarial Network

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Towards Continual Adaptation in Industrial Anomaly Detection

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Defect Image Sample Generation With Diffusion Prior for Steel Surface Defect Recognition

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A survey on RGB, 3D, and multimodal approaches for unsupervised industrial image anomaly detection

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PNPDet: Efficient Few-shot Detection without Forgetting via Plug-and-Play Sub-networks

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An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder

Yang, Q., & Guo, R. (2024) · Sensors · doi:10.3390/s24082440 · Cited by 35

A Novel Remote Visual Inspection System for Bridge Predictive Maintenance

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DG2GAN: improving defect recognition performance with generated defect image sample

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Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation

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GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data

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Simulation-Based Data Augmentation for the Quality Inspection of Structural Adhesive With Deep Learning

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Synthetic data in generalizable, learning-based neuroimaging

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Towards a Comprehensive Visual Quality Inspection for Industry 4.0*

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TF²: Few-Shot Text-Free Training-Free Defect Image Generation for Industrial Anomaly Inspection

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Unseen Visual Anomaly Generation

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Synthetic Data for Defect Segmentation on Complex Metal Surfaces

Fulir, J., Bosnar, L., Hagen, H., & Gospodnetić, P. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00465 · Cited by 22

Intraclass Image Augmentation for Defect Detection Using Generative Adversarial Neural Networks

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Diversified and Multi-Class Controllable Industrial Defect Synthesis for Data Augmentation and Transfer

Wei, J., Shen, F., Lv, C., Zhang, Z., Zhang, F., & Yang, H. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00467 · Cited by 21

Mask-Guided Generation Method for Industrial Defect Images with Non-uniform Structures

Wei, J., Zhang, Z., Shen, F., & Lv, C. (2022) · Machines · doi:10.3390/machines10121239 · Cited by 20

IndusSynthe: Synthetic data using human-machine intelligence hybrid for enhanced industrial surface defect detection through self-updating with multi-view filtering

Gong, Y., Liu, M., & Wang, X. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102253 · Cited by 19

Multi-stage few-shot micro-defect detection of patterned OLED panel using defect inpainting and multi-scale Siamese neural network

Ye, S., Wang, Z., Xiong, P., Xu, X., Du, L., Tan, J., & Wang, W. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02168-3 · Cited by 17

Long-Tailed Anomaly Detection with Learnable Class Names

Ho, C.-H., Peng, K., & Vasconcelos, N. M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01182 · Cited by 21

Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics

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DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection

Song, J., Park, D., Baek, K., Lee, S., Choi, J., Kim, E., & Yoon, S. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01744 · Cited by 18

A comprehensive survey of image synthesis approaches for Deep Learning-based surface defect detection in manufacturing

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Development of a high-fidelity failure prediction system for reinforced concrete bridge columns using generative adversarial networks

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Advancing Ultrasonic Defect Detection in High-Speed Wheels via UT-YOLO

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Human–machine knowledge hybrid augmentation method for surface defect detection based few-data learning

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DefectGAN: Synthetic Data Generation for EMU Defects Detection With Limited Data

Liu, S., Ni, H., Li, C., Zou, Y., & Luo, Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3386711 · Cited by 17

Image synthesis of apparel stitching defects using deep convolutional generative adversarial networks

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A non-destructive, autoencoder-based approach to detecting defects and contamination in reusable food packaging

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Few-Shot Defect Image Generation Based on Consistency Modeling

Shi, Q., Wei, J., Shen, F., & Zhang, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73116-7_21 · Cited by 17

Robust unsupervised-learning based crack detection for stamped metal products

Zhang, P., Ryu, H., Miao, Y., Jo, S., & Park, G. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.01.003 · Cited by 12

A mask guided cross data augmentation method for industrial defect detection

Wang, X., Li, W., & Lu, C. H. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107676 · Cited by 12

Generating Defective Epoxy Drop Images for Die Attachment in Integrated Circuit Manufacturing via Enhanced Loss Function CycleGAN

Alam, L., & Kehtarnavaz, N. (2023) · Sensors · doi:10.3390/s23104864 · Cited by 11

Intelligent IoT framework with GAN‐synthesized images for enhanced defect detection in manufacturing

Aramkul, S., & Sugunnasil, P. (2023) · Computational Intelligence · doi:10.1111/coin.12619 · Cited by 11

Synthesizing Industrial Defect Images Under Data Imbalance

Eun-Hee, C., Jeon, B., & Park, I. K. (2023) · IEEE Access · doi:10.1109/access.2023.3322927 · Cited by 10

On-the-fly image-level oversampling for imbalanced datasets of manufacturing defects

Theodoropoulos, S., Zajec, P., Rožanec, J. M., Kyriazis, D., & Tsanakas, P. D. (2024) · Machine Learning · doi:10.1007/s10994-023-06498-4 · Cited by 11

Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Railway Defect Detection

Ferdousi, R., Yang, C., Hossain, M. A., Laamarti, F., Hossain, M. S., & Saddik, A. E. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10283-3 · Cited by 10

Industrial and medical anomaly detection through cycle-consistent adversarial networks

Bougaham, A., Delchevalerie, V., Adoui, M. E., & Frénay‬, B. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128762 · Cited by 10

FEGAN: A Feature Extraction Based Approach for GAN Anomaly Detection and Localization

Fan, F., Zhang, L., & Dai, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3406438 · Cited by 11

A Timestep-Adaptive-Diffusion-Model-Oriented Unsupervised Detection Method for Fabric Surface Defects

Shancheng, T., Jin, Z., Zhang, Y., Lu, J., Li, H., & Jiqing, Y. (2023) · Processes · doi:10.3390/pr11092615 · Cited by 11

Residual generative adversarial network-driven Data enhancement for magnetic flux leakage-based defect recognition in oil and gas pipelines

Liu, B., Ding, L., He, L., Yang, L., Zhang, X., & Tian, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112720 · Cited by 9

Training-Free Industrial Defect Generation with Diffusion Models

Xu, R., Chiu, Y.-T., Chen, T.-I., Chew, O., Chuang, Y., & Cheng, W.-H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02244 · Cited by 9

Datasets and Methods for Boosting Infrastructure Inspection: A Survey on Defect Segmentation and Detection

Liu, K., Yang, G., Zhang, J., Zhao, Z., Chen, X., & Chen, B. M. (2022) · International Conference on Control and Automation · doi:10.1109/icca54724.2022.9831925 · Cited by 9

Defect Synthesis Using Latent Mapping Adversarial Network for Automated Visual Inspection

Song, S., Chang, K., Yun, K., Jun, C., & Baek, J. (2022) · Electronics · doi:10.3390/electronics11172763 · Cited by 9

Defect image generation through feature disentanglement using StyleGAN2-ADA

He, Z., Wu, K., & Wen, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130455 · Cited by 11

A Comprehensive Library for Benchmarking Multi-Class Visual Anomaly Detection

Zhang, J., He, H., 甘振业, He, Q., Cai, Y., Xue, Z., Wang, Y., Wang, C., Xie, L., & Liu, Y. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00373 · Cited by 32

Internal Thread Defect Generation Algorithm and Detection System Based on Generative Adversarial Networks and You Only Look Once

Jiang, Z., Dou, X., Liu, X., Xue, C., Wang, A., & Zhang, G. (2024) · Sensors · doi:10.3390/s24175636 · Cited by 8

A survey of generative models for image-based structural health monitoring in civil infrastructure

Gwon, G.-H., & Jung, H. (2025) · Journal of Infrastructure Intelligence and Resilience · doi:10.1016/j.iintel.2025.100138 · Cited by 9

AnomalyControl: Few-Shot Anomaly Generation by ControlNet Inpainting

Ali, M., Fioraio, N., Salti, S., & Stefano, L. D. (2024) · IEEE Access · doi:10.1109/access.2024.3520002 · Cited by 8

YOLOv8-SC: improving the YOLOv8 network for real-time detection of automotive coated surface defects

Ling, L., Zhu, C., Liu, M., Hu, J., Zhang, X., & Ge, M. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adb05b · Cited by 7

Improving the Fairness of the Min-Max Game in GANs Training

Zhang, Z., Yang, H., Wang, H., & McLoone, S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00289 · Cited by 7

Towards Accurate Unified Anomaly Segmentation

Ma, W., Yao, Q., Zhang, X., Huang, Z., Jiang, Z., & Zhou, S. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00138 · Cited by 10

Anomaly detection for surface of laptop computer based on PatchCore GAN algorithm

Zhu, H., Kang, Y., Zhao, Y., Yan, X., & Zhang, J. (2022) · Chinese Control Conference (CCC) · doi:10.23919/ccc55666.2022.9902712 · Cited by 7

Weakly Supervised Complex Texture Defect Detection Based on Nested U-Net Architecture

Huang, K., Zeng, Y., Zhao, J., Chai, S., & Yang, F. (2025) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-025-01161-5 · Cited by 6

Learning and Generating Diverse Residential Load Patterns Using GAN With Weakly-Supervised Training and Weight Selection

Liang, X., & Wang, H. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3563272 · Cited by 6

Generative Models Training Based on FEM Simulations Toward Improved Defect Detection

Tzelepakis, A., Dimitriou, N., Charalampous, P., Tsongas, K., Tzetzis, D., & Tzovaras, D. K. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3568966 · Cited by 6

Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection

Kruse, M., & Rosenhahn, B. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00378 · Cited by 12

Enhancing Battery Exterior Defect Inspection Accuracy Through Defect-Background Separated GAN Development

Ku, D., & Pahk, H.-J. (2024) · IEEE Access · doi:10.1109/access.2024.3380618 · Cited by 6

Advancing Architectural Design Through Generative Adversarial Network Deep Learning Technology

Huang, Y., & He, J. (2024) · International Journal of Distributed Systems and Technologies · doi:10.4018/ijdst.353305 · Cited by 6

Increasing the Generalization of Supervised Fabric Anomaly Detection Methods to Unseen Fabrics

Rippel, O., Zwinge, C., & Merhof, D. (2022) · Sensors · doi:10.3390/s22134750 · Cited by 6

Robust AD: A Real World Benchmark Dataset for Robustness in Industrial Anomaly Detection

Pemula, L., Zhang, D., & Dabeer, O. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00389 · Cited by 5

High-quality matched transfer generation adversarial network for synthetic cross-material surface defect images

Xie, X., Li, C., Qing, R., Zhou, C., & Zhang, Z. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104441 · Cited by 5

Utilizing generative AI for crack detection in the marble industry

Vrochidou, Ε., Sidiropoulos, G. K., Ouzounis, A. G., Tsimperidis, I., Kalpakis, V., Stamkos, A., & Papakostas, G. A. (2025) · Engineering Research Express · doi:10.1088/2631-8695/adaca7 · Cited by 6

Automatic Inspection of Seal Integrity in Sterile Barrier Packaging: A Deep Learning Approach

Diaz, J. Z., Farooq, M. A., & Corcoran, P. (2024) · IEEE Access · doi:10.1109/access.2023.3348779 · Cited by 5

Detection of Stain Defects in Textile Industry using State-of-Art Transfer Learning Models

Karegowda, A. G., Pooja, R., Rani, A. L., & Devika, G. (2024) · International Conference on Smart Systems for Applications in Electrical Sciences (ICSSES) · doi:10.1109/icsses62373.2024.10561384 · Cited by 5

Unbalanced Feature Transport for Exemplar-based Image TranslationPreprint

Zhan, F. (2021) · doi:10.31219/osf.io/f6vur · Cited by 5

Component-Aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection

Tong, X., Chang, Y., Zhao, Q., Yu, J., Wang, B., Lin, J., Lin, Y., Mai, X., Wang, H., Tao, Z., et al. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127296 · Cited by 7

DefectGen: Few-Shot Defect Image Generation using Stable Diffusion for Steel Surface Analysis

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Synth4Seg—Learning Defect Data Synthesis for Defect Segmentation Using Bi-Level Optimization

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