Surface Defect Saliency of Magnetic Tile

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

A Combined Corner and Edge Detector

Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898

A model of saliency-based visual attention for rapid scene analysis

Itti, L., Christof Koch, A., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,289

Deformable Convolutional Networks

Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513

Frequency-tuned salient region detection

Achanta, R., Hemami, S. S., Estrada, F., & Susstrunk, S. E. (2009) · 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/cvpr.2009.5206596 · Cited by 4,364

Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295

Saliency Detection via Graph-Based Manifold Ranking

Yang, C., Zhang, L., Lu, H., Ruan, X., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.407 · Cited by 2,554

Salient Object Detection: A Benchmark

Borji, A., Cheng, M.-M., Jiang, H., & Li, J. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2487833 · Cited by 1,550

Towards Total Recall in Industrial Anomaly Detection

Roth, K., Pemula, L., Zepeda, J., Scholkopf, B., Brox, T., & Gehler, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01392 · Cited by 1,810

The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., & Steger, C. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01400-4 · Cited by 602

PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection

Dong, H., Song, K., He, Y., Xu, J., Yan, Y., & Meng, Q. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2958826 · Cited by 559

Surface Defect Detection Methods for Industrial Products: A Review

Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021) · Applied Sciences · doi:10.3390/app11167657 · Cited by 457

EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies

Batzner, K., Heckler, L., & König, R. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00020 · Cited by 469

Image-Based Surface Defect Detection Using Deep Learning: A Review

Bhatt, P. M., Malhan, R. K., Rajendran, P. S., Shah, B. C., Thakar, S., Yoon, Y. J., & Gupta, S. K. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4049535 · Cited by 392

Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review

Saberironaghi, A., Ren, J., & El–Gindy, M. (2023) · Algorithms · doi:10.3390/a16020095 · Cited by 343

Triplet-Graph Reasoning Network for Few-Shot Metal Generic Surface Defect Segmentation

Bao, Y., Song, K., Liu, J., Wang, Y., Yan, Y., Yu, H., & Li, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3083561 · Cited by 322

Synthetic data augmentation for surface defect detection and classification using deep learning

Jain, S., Seth, G., Paruthi, A., Soni, U., & Kumar, G. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01710-x · Cited by 302

Recent advances in surface defect inspection of industrial products using deep learning techniques

Zheng, X., Zheng, S., Kong, Y., & Chen, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06592-8 · Cited by 287

Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization

Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., & Steger, C. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01578-9 · Cited by 286

Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions

Ježek, Š., Jonák, M., Bürget, R., Dvořák, P., & Skotak, M. (2021) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt54235.2021.9631567 · Cited by 202

Surface Defect Detection via Entity Sparsity Pursuit With Intrinsic Priors

Wang, J., Li, Q., Gan, J., Yu, H., & Yang, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2917522 · Cited by 141

SDDNet: A Fast and Accurate Network for Surface Defect Detection

Cui, L., Jiang, X., Xu, M., Li, W., Lv, P., & Zhou, B. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3056744 · Cited by 132

A survey of real-time surface defect inspection methods based on deep learning

Liu, Y., Zhang, C., & Dong, X. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10475-7 · Cited by 136

Surface defect detection methods for industrial products with imbalanced samples: A review of progress in the 2020s

Bai, D., Li, G., Jiang, D., Yun, J., Tao, B., Jiang, G., Sun, Y., & Ju, Z. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107697 · Cited by 98

Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection

Liu, J., Song, K., Feng, M., Yan, Y., Tu, Z., & Liu, Z. (2020) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2020.106324 · Cited by 95

TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions

Tan, D. S., Chen, Y.-C., Chen, T. P.-C., & Chen, W.-C. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00032 · Cited by 85

One-Shot Recognition of Manufacturing Defects in Steel Surfaces

Deshpande, A. M., Minai, A. A., & Kumar, M. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.05.146 · Cited by 73

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Dong, X., Taylor, C., & Cootes, T. F. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3031188 · Cited by 69

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Dong, X., Taylor, C., & Cootes, T. F. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3039115 · Cited by 66

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Dong, X., Taylor, C., & Cootes, T. F. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3109353 · Cited by 64

SCueU-Net: Efficient Damage Detection Method for Railway Rail

Lu, J., Liang, B., Lei, Q., Li, X., Liu, J., Ji, L., Xu, J., & Wang, W. (2020) · IEEE Access · doi:10.1109/access.2020.3007603 · Cited by 63

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Yang, B., Liu, Z., Duan, G., & Tan, J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3126098 · Cited by 61

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Cao, Y., Xu, X., Cheng, Y., Sun, C., Du, Z., Gao, L., & Shen, W. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3536165 · Cited by 63

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Chen, Z., Deng, J., Zhu, Q., Wang, H., & Chen, Y. (2022) · Electronics · doi:10.3390/electronics11152383 · Cited by 55

Industrial object and defect recognition utilizing multilevel feature extraction from industrial scenes with Deep Learning approach

Apostolopoulos, I. D., & Tzani, M. A. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03688-7 · Cited by 53

DDSNet: Deep Dual-Branch Networks for Surface Defect Segmentation

Yin, Z., Qin, L., Han, G., Shi, X., Zhang, F., Xu, G., & Bi, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3427806 · Cited by 36

Unsupervised Defect Segmentation of Magnetic Tile Based on Attention Enhanced Flexible U-Net

Cao, X., Chen, B., & He, W. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3170989 · Cited by 33

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Shukla, V., Shukla, A., K, S. P. S., & Shukla, S. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1554196 · Cited by 39

Deep Learning and Laser-Based 3-D Pixel-Level Rail Surface Defect Detection Method

Ye, J., Stewart, E., Chen, Q., Roberts, C., Hajiyavand, A. M., & Lei, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3272033 · Cited by 29

Semisupervised Defect Segmentation With Pairwise Similarity Map Consistency and Ensemble-Based Cross Pseudolabels

Sime, D. M., Wang, G., Zeng, Z., Wang, W., & Peng, B. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3230785 · Cited by 27

Advances in Deep Learning for Semantic Segmentation of Low-Contrast Images: A Systematic Review of Methods, Challenges, and Future Directions

Urrea, C., & Vélez, M. (2025) · Sensors · doi:10.3390/s25072043 · Cited by 25

EGD-Net: Edge-guided and differential attention network for surface defect detection

Zhang, E., Ma, Q., Chen, Y., Duan, J., & Shao, L. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100403 · Cited by 26

SCL—Segmentation–Classification combined Loss for surface defect detection

Versini, E., Snidaro, L., & Liani, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116710 · Cited by 25

Detail texture detection based on Yolov4‐tiny combined with attention mechanism and bicubic interpolation

Hui, T., Xu, Y., & Rasol, J. (2021) · IET Image Processing · doi:10.1049/ipr2.12228 · Cited by 24

Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection

Wan, Q., Gao, L., & Li, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3205674 · Cited by 22

The implication and evaluation of geometrical imperfections on manufactured surfaces

Mullany, B. A., Savio, E., Haitjema, H., & Leach, R. (2022) · CIRP Annals · doi:10.1016/j.cirp.2022.05.004 · Cited by 22

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

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Zhou, M. Y., Wu, T., Xia, Z., He, B., Kong, L. B., & Su, H. (2024) · Measurement · doi:10.1016/j.measurement.2024.115956 · Cited by 26

A linearized framework and a new benchmark for model selection for fine-tuningPreprint

Deshpande, A., Achille, A., Ravichandran, A., Li, H., Zancato, L., Fowlkes, C. C., Bhotika, R., Soatto, S., & Perona, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00084 · Cited by 45

JujubeNet: A high-precision lightweight jujube surface defect classification network with an attention mechanism

Jiang, L., Yuan, B., Ma, W., & Wang, Y. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2022.1108437 · Cited by 21

MSF-Net: Multi-Scale Feature Learning Network for Classification of Surface Defects of Multifarious Sizes

Xu, P., Guo, Z., Liang, L., & Xu, X. (2021) · Sensors · doi:10.3390/s21155125 · Cited by 20

An efficient model for metal surface defect detection based on attention mechanism and multi-scale feature

Zhang, H., Fu, W., Wang, X., Li, D., Zhu, D., & Su, X. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06591-y · Cited by 22

Adaptive Cross-Scenario Few-Shot Learning Framework for Structural Damage Detection in Civil Infrastructure

Xue, G., Liu, S., Ren, L., & Gong, D. (2023) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-13196 · Cited by 17

Small Sample Image Segmentation by Coupling Convolutions and Transformers

Qi, H., Zhou, H., Dong, J., & Dong, X. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3343632 · Cited by 16

Multi-dimensional feature extraction-based deep encoder–decoder network for automatic surface defect detection

Üzen, H., Türkoğlu, M., & Hanbay, D. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07885-z · Cited by 15

Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks

Cheng, J., Tan, W., Yuan, Y., Zhao, Z., & Cheng, Y. (2024) · Applied Sciences · doi:10.3390/app14104161 · Cited by 12

CDARNet: A robust cross-dimensional adaptive region reconstruction network for real-time metal surface defect segmentation

Li, Q., Ding, C., Wang, B., Jiao, J., Huang, W., & Zhu, Z. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103514 · Cited by 11

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Seibold, C., Künzel, J., Hilsmann, A., & Eisert, P. (2022) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010893600003124 · Cited by 11

Semantic Segmentation Network for Surface Defect Detection of Automobile Wheel Hub Fusing High-Resolution Feature and Multi-Scale Feature

Tang, C., Feng, X., Wen, H., Xu, Z., Shao, Y., Zhou, X., Huang, B., & Li, Y. (2021) · Applied Sciences · doi:10.3390/app112210508 · Cited by 10

Foreground–background separation transformer for weakly supervised surface defect detection

Jiang, X., Feng, J., Feng, Y., Lu, Y., Fa, Q., Zhang, W., & Xu, M. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02446-8 · Cited by 10

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Li, Q., Wu, T., Xu, T., Lei, J., & Liu, J. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00894-5 · Cited by 10

Balanced Loss Function for Accurate Surface Defect Segmentation

Xie, Z., Shu, C., Fu, Y., Zhou, J., & Chen, D. (2023) · Applied Sciences · doi:10.3390/app13020826 · Cited by 12

FASCNet: An Edge-Computational Defect Detection Model for Industrial Parts

Li, J., Wu, R., Zhang, S., Chen, Y., & Dong, Z. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3313164 · Cited by 9

A Surface Defect Detection Method Via Fusing Multi-Level Features

Ren, M., Shen, R., & Gong, Y. (2022) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4053520 · Cited by 9

High-Precision Surface Crack Detection for Rolling Steel Production Equipment in ICPS

Peng, Y., Wang, C., Hao, Y., Zhen, L., Chen, G., Kumar, N., & Yu, K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3302317 · Cited by 8

Unraveling False Positives in Unsupervised Defect Detection Models: A Study on Anomaly-Free Training Datasets

Qiu, J. W., Shi, H., Hu, Y. H., & Yu, Z. (2023) · Sensors · doi:10.3390/s23239360 · Cited by 11

Purposive Data Augmentation Strategy and Lightweight Classification Model for Small Sample Industrial Defect Dataset

Lin, L., Zhao, S., Zhang, Y., Wen, A., Zhang, S., Yan, J., Wang, Y., & Zhou, Y. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3404053 · Cited by 7

A Study on Data Augmentation Techniques for Visual Defect Detection in Manufacturing

Leyendecker, L., Agarwal, S., Werner, T., Motz, M., & Schmitt, R. H. (2023) · Technologien für die intelligente Automation · doi:10.1007/978-3-662-66769-9_6 · Cited by 11

Multi-defect detection for magnetic tile based on SE-U-Net

Cao, X., Yao, B., Chen, B., & Wang, Y. (2020) · IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA) · doi:10.1109/ispce-cn51288.2020.9321855 · Cited by 7

A Four-Stage Product Appearance Defect Detection Method With Small Samples

Xie, X., Zhang, R., Peng, L., & Peng, S. (2022) · IEEE Access · doi:10.1109/access.2022.3196935 · Cited by 7

One-stage object detection networks for inspecting the surface defects of magnetic tiles

Wei, J., Zhu, P., Qian, X., & Zhu, S. (2019) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist48021.2019.9010098 · Cited by 7

MTGGAN: generate type-controllable magnetic tile defect data to improve defect detection performance

Jiang, C., Xu, B., Li, Z., Zhang, L., Sun, Q. S., & Zhang, D. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2419895 · Cited by 6

Category-Agnostic Cluster-Guided Selective Suppression for Weakly Supervised Surface Defect Localization

Jiang, S., Liu, M., Liu, Y., Ma, Y., & Wang, Y. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3547122 · Cited by 5

OneN: Guided attention for natively-explainable anomaly detection

Coscia, P., Genovese, A., Piuri, V., & Scotti, F. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105741 · Cited by 4

Referring Solar Cell Defect Segmentation in Electroluminescence Images

Dong, S., Zhang, J., Shen, Y. M., & Gao, D. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3567396 · Cited by 4

Reconstruction-Based Anomaly Localization via Knowledge-Informed Self-Training

Cheng, Q., Lao, X., & Li, C. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3542229 · Cited by 4

Metal Surface Defect Detection using Object Detection Models

Shah, H., & Patil, V. (2023) · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · doi:10.1109/iitcee57236.2023.10090466 · Cited by 4

Memory-efficient and GPU-oriented visual anomaly detection with incremental dimension reduction

Lee, T., Nagai, Y., & Minezawa, A. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00292 · Cited by 4

SGTP-Net: Semantic Guidance and Texture Priors-Based Dual-Branch Segmentation Network for Surface Defect Detection

Zhang, S., Jiang, L., Ge, L., Wu, C., Wang, Y., Lu, K., & Zhang, C. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3639293 · Cited by 3

LRTISS: a lightweight real-time network for industrial surface defect segmentation

He, Z., Chen, Y., zhang, Z., & Huang, Z. (2026) · Measurement · doi:10.1016/j.measurement.2026.121410 · Cited by 3

Small sample pipeline DR defect detection based on smooth variational autoencoder and enhanced detection head faster RCNN

Zhang, T., You, T., Liu, Z., Rehman, S. U., Shi, Y., & Munshi, A. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06590-3 · Cited by 3

Relational-based transfer learning for automatic optical inspection based on domain discrepancy

Salgado, E. I. V., Yan, H., Hong, Y., Zhu, P., Zhu, S., Liao, C., Wen, Y., Li, X., Qian, X., Wang, X., et al. (2022) · Optoelectronic Imaging and Multimedia Technology · doi:10.1117/12.2644087 · Cited by 3

Multi-Scale Feature Distillation for Anomaly Detection

Yao, X., Li, R., Zhang, C., Huang, K., & Sun, K. (2021) · International Conference on Mechatronics and Machine Vision in Practice (M2VIP) · doi:10.1109/m2vip49856.2021.9665143 · Cited by 3

Fault Detection and Identification Based on Image Processing and Deep Learning

Jarndal, A., Mahmoud, M. S., & Abbas, O. M. (2022) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset53988.2022.9734799 · Cited by 3

Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Maps

Caruso, E., Pelosin, F., Simoni, A., & Lanz, O. (2026) · Journal of Imaging · doi:10.3390/jimaging12030132 · Cited by 2

Anomaly Detection of Integrated Circuits Package Substrates Using the Large Vision Model SAIC: Dataset Construction, Methodology, and Application

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Anomaly detection for real-world industrial applications: benchmarking recent self-supervised and pretrained methods

Ježek, Š., Jonák, M., Bürget, R., Dvořák, P., & Skotak, M. (2022) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt57764.2022.9943437 · Cited by 2

A StyleGAN3-Based Data Augmentation Method for Ceramic Defect Detection

Ou, H., An, J., Wang, X.-A., Xiong, J., Chen, X., & Wang, Q. (2023) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps58381.2023.10128067 · Cited by 2

Cross-Domain Defect Detection Network

Zhou, Z., Lan, C., & Gao, Z. (2022) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1109/cacml55074.2022.00053 · Cited by 2

Lightweight Reconstruction Network for Surface Defect Detection Based on Texture Complexity Analysis

Shi, H., Li, G., & Bao, H. (2023) · Electronics · doi:10.3390/electronics12173617 · Cited by 2

Segment Anything Model for Industrial Vision: A Comprehensive Evaluation with a New Metric, a New Dataset, and a ToolboxPreprint

Zhong, X., Hu, C., Xie, J., Yi, J., Zhou, Y., Zeng, D., Zhou, L., Chen, K., Liu, W., & Wu, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-4257722/v1 · Cited by 3

Bildverarbeitung in der Automation

Lohweg, V. (2023) · Technologien für die intelligente Automation · doi:10.1007/978-3-662-66769-9 · Cited by 1

A Lightweight Conditional Diffusion Segmentation Network Based on Deformable Convolution for Surface Defect Detection

Chen, J., Zhao, Y., & Wang, H. (2025) · Journal of Electrical and Computer Engineering · doi:10.1155/jece/2935790 · Cited by 1

Generating realistic 3D surface defects for training AI-Based industrial inspection systems

Toribio, S. R. H., Roos-Hoefgeest, M., Garcia, D., Álvarez, I., & González, R. C. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16851-7 · Cited by 1

SPGDD-GPT: Image-Text-Driven Generic Defect Diagnosis Using a Self-Prompted Large Vision-Language Model

An, S., & Dong, X. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3657596 · Cited by 1

Benchmarking Few-Shot Learning Techniques for Steel Surface Defect Detection

Ghali, R., Benhafid, Z., & Selouani, S. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00031 · Cited by 1

SFCW-Net: a spatial-frequency-domain coupling wavelet network for industrial surface defect detection

Xiang, Z., Xu, Z., Wu, Q., & Wu, W. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-026-02846-y · Cited by 1

Fine-Grained Hierarchical Perception Siamese Network for Battery Surface Defect Detection

Xu, Y., Chen, Y., Zhai, Y., Pan, W., Zhu, H., Zhou, J., Wang, T., Coscia, P., Genovese, A., & Chen, C. L. P. (2026) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2026.3692529 · Cited by 1

Weakly supervised surface defect classification network based on Gaussian model

Song, K., Feng, H., Peng, C., Zhao, M. L., Tian, X., Liao, X., & Xiao, J. (2022) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp54964.2022.9778408 · Cited by 1

Evaluation of Nested U-Net models performance on MVTec AD dataset

Jonák, M., Ježek, Š., & Bürget, R. (2022) · International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) · doi:10.1109/icumt57764.2022.9943348 · Cited by 1

Semi-Supervised Framework for Rail Track Surface Damage Detection : Rail track surface damage detected by RTDS-Net

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