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

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Support-Vector Networks

Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works

Untitled work

(2016) · Cited by 31,590 · see citing works

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755 · see citing works

Fast R-CNN

Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works

Textural Features for Image Classification

Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works

SSD: Single Shot MultiBox Detector

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128 · see citing works

Classification and Regression Trees.

Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · see citing works

Advances in neural information processing systems 7

(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00716 · Cited by 9,982 · see citing works

Extracting and composing robust features with denoising autoencoders

Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436 · see citing works

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_8 · Cited by 7,356 · cited 2× · see citing works

A comparative study of texture measures with classification based on featured distributions

Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882 · see citing works

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv · doi:10.48550/arxiv.1602.07360 · Cited by 6,201 · see citing works

An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression

Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,721 · see citing works

Semi-Supervised Learning

Chapelle, O., Schölkopf, B., & Zien, A. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262033589.001.0001 · Cited by 4,560 · see citing works

MixMatch: A Holistic Approach to Semi-Supervised LearningPreprint

Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., & Raffel, C. A. (2019) · Neural Information Processing Systems · doi:10.48550/arxiv.1905.02249 · Cited by 3,744 · see citing works

A Survey on Deep Transfer Learning

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01424-7_27 · Cited by 3,165 · see citing works

Learning internal representations by error propagation

RUMELHART, D. E., HINTON, G. E., & WILLIAMS, R. J. (1988) · Readings in Cognitive Science · doi:10.1016/b978-1-4832-1446-7.50035-2 · Cited by 3,002 · see citing works

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,561 · see citing works

Markov Random Fields and Their Applications

Kindermann, R. P., & Snell, J. L. (1980) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/001 · Cited by 1,292 · see citing works

A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects

Song, K., & Yan, Y. (2013) · Applied Surface Science · doi:10.1016/j.apsusc.2013.09.002 · Cited by 1,026 · see citing works

A survey on industrial vision systems, applications and tools

Malamas, E. N., Petrakis, E. G. M., Zervakis, M. E., Petit, L., & Legat, J.-D. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(02)00152-x · Cited by 798 · see citing works

Computer-Vision-Based Fabric Defect Detection: A Survey

Kumar, A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.1930.896476 · Cited by 725 · see citing works

A Survey of Automated Visual Inspection

Newman, T. S., & Jain, A. K. (1995) · Computer Vision and Image Understanding · doi:10.1006/cviu.1995.1017 · Cited by 619 · see citing works

Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection

Weimer, D., Scholz-Reiter, B., & Shpitalni, M. (2016) · CIRP Annals · doi:10.1016/j.cirp.2016.04.072 · Cited by 577 · see citing works

Automated fabric defect detection—A review

Ngan, H. Y. T., Pang, G. K. H., & Yung, N. H. C. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.02.002 · Cited by 550 · see citing works

Semi-Supervised Learning with Generative Adversarial NetworksPreprint

Odena, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01583 · Cited by 539 · see citing works

Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images

Deitsch, S., Christlein, V., Berger, S., Buerhop-Lutz, C., Maier, A., Gallwitz, F., & Riess, C. (2018) · Solar Energy · doi:10.1016/j.solener.2019.02.067 · Cited by 516 · see citing works

A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques

Xie, X. (2008) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.268 · Cited by 500 · see citing works

A Generic Deep-Learning-Based Approach for Automated Surface Inspection

Ren, R., Hung, T., & Tan, K. C. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2668395 · Cited by 485 · see citing works

A fast and robust convolutional neural network-based defect detection model in product quality control

Wang, T., Chen, Y., Qiao, M., & Snoussi, H. (2017) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0882-0 · Cited by 481 · see citing works

Machine learning-based image processing for on-line defect recognition in additive manufacturing

Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R. X., & Teti, R. (2019) · CIRP Annals · doi:10.1016/j.cirp.2019.03.021 · Cited by 423 · see citing works

Wafer Map Defect Pattern Classification and Image Retrieval Using Convolutional Neural Network

Nakazawa, T., & Kulkarni, D. V. (2018) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2018.2795466 · Cited by 412 · see citing works

Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets

Wu, M.-J., Jang, J. R., & Chen, J.-L. (2014) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2014.2364237 · Cited by 407 · see citing works

Automatic PCB Inspection Algorithms: A Survey

Moganti, M., Erçal, F., Dağli, C. H., & Tsunekawa, S. (1996) · Computer Vision and Image Understanding · doi:10.1006/cviu.1996.0020 · Cited by 404 · see citing works

Design and application of industrial machine vision systems

Golnabi, H., & Asadpour, A. (2007) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2007.02.005 · Cited by 389 · see citing works

Rigid-Motion Scattering for Texture ClassificationPreprint

Sifre, L., & Mallat, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.1687 · Cited by 371 · see citing works

Real-time detection of steel strip surface defects based on improved yolo detection network

Li, J., Su, Z., Geng, J., & Yin, Y. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.09.412 · Cited by 371 · see citing works

Computer vision algorithms and hardware implementations: A survey

Feng, X., Jiang, Y., Yang, X., Du, M., & Li, X. S. (2019) · Integration · doi:10.1016/j.vlsi.2019.07.005 · Cited by 360 · see citing works

Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

Li, Y., Huang, H., Xie, Q., Yao, L., & Chen, Q. (2018) · Applied Sciences · doi:10.3390/app8091678 · Cited by 325 · see citing works

Review of vision-based steel surface inspection systems

Neogi, N., Mohanta, D. K., & Dutta, P. K. (2014) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2014-50 · Cited by 322 · cited 2× · see citing works

Automated inspection of textile fabrics using textural models

Cohen, F. S., Fan, Z., & Attali, S. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.85670 · Cited by 321 · see citing works

Machine learning-based imaging system for surface defect inspection

Park, J.-K., Kwon, B.-K., Park, J.-H., & Kang, D.-J. (2016) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-016-0039-x · Cited by 311 · see citing works

Convolutional Neural Network for Wafer Surface Defect Classification and the Detection of Unknown Defect Class

Cheon, S., Lee, H., Kim, C. O., & Lee, S. H. (2019) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2019.2902657 · Cited by 309 · see citing works

Automated visual inspection in the semiconductor industry: A survey

Huang, S.-H., & Pan, Y.-C. (2015) · Computers in Industry · doi:10.1016/j.compind.2014.10.006 · Cited by 307 · see citing works

Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images

Soukup, D., & Huber-Mörk, R. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14249-4_64 · Cited by 300 · see citing works

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Mei, S., Wang, Y., & Wen, G. (2018) · Sensors · doi:10.3390/s18041064 · Cited by 284 · see citing works

Fabric defect detection systems and methods—A systematic literature review

Hanbay, K., Talu, M. F., & Özgüven, Ö. F. (2016) · Optik · doi:10.1016/j.ijleo.2016.09.110 · Cited by 268 · see citing works

Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning

Li, Y., Zhao, W., & Pan, J. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2520955 · Cited by 265 · see citing works

Automated defect inspection of LED chip using deep convolutional neural network

Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-018-1415-x · Cited by 241 · see citing works

Surface defect classification of steels with a new semi-supervised learning method

Di, H., Ke, X., Peng, Z., & Dongdong, Z. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.01.011 · Cited by 241 · see citing works

A Hierarchical Extractor-Based Visual Rail Surface Inspection System

Gan, J., Li, Q., Wang, J., & Yu, H. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2761858 · Cited by 240 · see citing works

Neural network based detection of local textile defects

Kumar, A. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(03)00005-0 · Cited by 230 · see citing works

Decision Tree Ensemble-Based Wafer Map Failure Pattern Recognition Based on Radon Transform-Based Features

Piao, M., Jin, C., Lee, J. Y., & Byun, J. (2018) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2018.2806931 · Cited by 201 · see citing works

A semi-supervised convolutional neural network-based method for steel surface defect recognition

Gao, Y., Gao, L., Li, X., & Yan, X. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101825 · Cited by 189 · see citing works

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Luo, Q., Sun, Y., Li, P., Simpson, O., Tian, L., & He, Y. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2852918 · Cited by 178 · see citing works

An intelligent real-time vision system for surface defect detection

Jia, H., Murphey, Y. L., Shi, J., & Chang, T.-S. (2004) · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · doi:10.1109/icpr.2004.1334512 · Cited by 174 · see citing works

Online PCB Defect Detector On A New PCB Defect DatasetPreprint

Tang, S., He, F., Huang, X., & Yang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06197 · Cited by 166 · see citing works

Steel Surface Defect Detection Using a New Haar–Weibull-Variance Model in Unsupervised Manner

Liu, K., Wang, H., Chen, H., Qu, E., Tian, Y., & Sun, H. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2712838 · Cited by 161 · see citing works

Domain adaptation and sample bias correction theory and algorithm for regression

Cortes, C., & Mohri, M. (2013) · Theoretical Computer Science · doi:10.1016/j.tcs.2013.09.027 · Cited by 147 · see citing works

A novel scene classification model combining ResNet based transfer learning and data augmentation with a filter

Liu, S., Tian, G., & Xu, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.090 · Cited by 147 · see citing works

Fabric Defect Detection Using Modified Local Binary Patterns

Tajeripour, F., Kabir, E., & Sheikhi, A. (2007) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2008/783898 · Cited by 146 · see citing works

Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning

Yang, J., Li, S., Wang, Z., & Yang, G. (2019) · IEEE Access · doi:10.1109/access.2019.2925561 · Cited by 145 · see citing works

Segmentation of textured images using a multiresolution Gaussian autoregressive model

Comer, M. L., & Delp, E. J. (1999) · IEEE Transactions on Image Processing · doi:10.1109/83.748895 · Cited by 142 · see citing works

Wavelet-based defect detection in solar wafer images with inhomogeneous texture

Li, W.-C., & Tsai, D. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.07.025 · Cited by 127 · see citing works

Differential evolution-based optimal Gabor filter model for fabric inspection

Tong, L., Wong, W. K., & Kwong, C. K. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.09.011 · Cited by 118 · see citing works

Wafer Classification Using Support Vector Machines

Baly, R., & Hajj, H. M. (2012) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2012.2196058 · Cited by 117 · see citing works

Surface Defect Saliency of Magnetic Tile

Huang, Y., Qiu, C., Guo, Y., Wang, X., & Yuan, K. (2018) · 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2018.8560423 · Cited by 117 · see citing works

Automatic fabric defect detection with a wide-and-compact network

Li, Y., Zhang, D., & Lee, D.-J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.070 · Cited by 114 · see citing works

Research Progress of Visual Inspection Technology of Steel Products—A Review

Sun, X., Gu, J., Tang, S., & Li, J. (2018) · Applied Sciences · doi:10.3390/app8112195 · Cited by 109 · see citing works

Transfer-Learning-Based Online Mura Defect Classification

Yang, H. S., Mei, S., Song, K., Tao, B., & Yin, Z. (2017) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2017.2777499 · Cited by 108 · see citing works

Automated defect detection in uniform and structured fabrics using Gabor filters and PCA

Bissi, L., Baruffa, G., Placidi, P., Ricci, E., Scorzoni, A., & Valigi, P. (2013) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2013.05.011 · Cited by 102 · see citing works

A postural information based biometric authentification system employing S-transform, radial basis network and Kalman filtering

Chatterjee, A., Fournier, R., Naït-Ali, A., & Siarry, P. (2010) · HAL · Cited by 97 · see citing works

Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm

Jeyaraj, P. R., & Nadar, E. R. S. (2019) · International Journal of Clothing Science and Technology · doi:10.1108/ijcst-11-2018-0135 · Cited by 95 · see citing works

Surface defect classification in large-scale strip steel image collection via hybrid chromosome genetic algorithm

Hu, H., Liu, Y., Liu, M., & Nie, L. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.05.134 · Cited by 93 · see citing works

Building an Automatic Defect Verification System Using Deep Neural Network for PCB Defect Classification

Deng, Y.-S., Luo, A.-C., & Dai, M.-J. (2018) · International Conference on Frontiers of Signal Processing (ICFSP) · doi:10.1109/icfsp.2018.8552045 · Cited by 85 · see citing works

Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage

Hu, G., Wang, Q., & Zhang, G. (2015) · Applied Optics · doi:10.1364/ao.54.002963 · Cited by 83 · see citing works

Automated defect detection in textured surfaces using optimal elliptical Gabor filters

Hu, G. (2015) · Optik · doi:10.1016/j.ijleo.2015.04.017 · Cited by 78 · see citing works

Development of a machine vision system: real-time fabric defect detection and classification with neural networks

Çelık, H. İ., Dülger, L. C., & Topalbekiroğlu, M. (2013) · Journal of the Textile Institute · doi:10.1080/00405000.2013.827393 · Cited by 78 · see citing works

Surface Defects Classification of Hot-Rolled Steel Strips Using Multi-directional Shearlet Features

Ashour, M. W., Khalid, F., Abdul Halin, A., Abdullah, L. N., & Darwish, S. H. (2019) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-018-3329-5 · Cited by 77 · see citing works

CNN‐based reference comparison method for classifying bare PCB defects

Peng, W., Liu, C., Liu, M., Gao, Y., & Liu, H. (2018) · The Journal of Engineering · doi:10.1049/joe.2018.8271 · Cited by 77 · see citing works

Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Luo, Q., Fang, X., Sun, Y., Liu, L., Ai, J., Yang, C., & Simpson, O. (2019) · IEEE Access · doi:10.1109/access.2019.2898215 · Cited by 75 · see citing works

Fabric defect detection based on saliency histogram features

Li, M., Wan, S., Deng, Z., & Wang, Y. (2019) · Computational Intelligence · doi:10.1111/coin.12206 · Cited by 70 · see citing works

A survey on defect detection in bare PCB and assembled PCB using image processing techniques

Anitha, D. B., & Rao, M. K. (2017) · International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet.2017.8299715 · Cited by 65 · see citing works

Fine-Grained Material Classification Using Micro-geometry and Reflectance

Kampouris, C., Zafeiriou, S. P., Ghosh, A., & Malassiotis, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_47 · Cited by 64 · see citing works

Optimization of automated online fabric inspection by fast Fourier transform (FFT) and cross-correlation

Malek, A. S., Dréan, J.-Y., Bigué, L., & Osselin, J.-F. (2012) · Textile Research Journal · doi:10.1177/0040517512458340 · Cited by 62 · see citing works

Surface defects inspection of cold rolled strips based on neural network

Kang, G.-W., & Liu, H.-B. (2005) · 2005 International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2005.1527830 · Cited by 62 · see citing works

A Deep Learning Model for Identification of Defect Patterns in Semiconductor Wafer Map

Yang, Y.-F. (2019) · Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) · doi:10.1109/asmc.2019.8791815 · Cited by 61 · see citing works

A New Ensemble Approach based on Deep Convolutional Neural Networks for Steel Surface Defect classification

Chen, W., Gao, Y., Gao, L., & Li, X. (2018) · Procedia CIRP · doi:10.1016/j.procir.2018.03.264 · Cited by 56 · see citing works

Deep Learning-Based Wafer-Map Failure Pattern Recognition Framework

Ishida, T., Nitta, I., Fukuda, D., & Kanazawa, Y. (2019) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed.2019.8697407 · Cited by 56 · see citing works

A Generic Semi-Supervised Deep Learning-Based Approach for Automated Surface Inspection

Zheng, X., Wang, H., Chen, J., Kong, Y., & Zheng, S. (2020) · IEEE Access · doi:10.1109/access.2020.3003588 · Cited by 56 · see citing works

Detection of Pinholes in Steel Slabs Using Gabor Filter Combination and Morphological Features

Choi, D.-C., Jeon, Y.-J., Kim, S. H., Moon, S., Yun, J. P., & Kim, S. W. (2017) · ISIJ International · doi:10.2355/isijinternational.isijint-2016-160 · Cited by 55 · see citing works

Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks

Song, L., Li, X., Yang, Y., Zhu, X., Guo, Q., & Yang, H. (2018) · Sensors · doi:10.3390/s18113709 · Cited by 55 · see citing works

Fabric defects detection using adaptive wavelets

Wen, Z., Cao, J., Liu, X., & Ying, S. (2014) · International Journal of Clothing Science and Technology · doi:10.1108/ijcst-03-2013-0031 · Cited by 54 · see citing works

Automatic Visual Defect Detection Using Texture Prior and Low-Rank Representation

Huangpeng, Q., Zhang, H., Zeng, X., & Huang, W. (2018) · IEEE Access · doi:10.1109/access.2018.2852663 · Cited by 53 · see citing works

Industrial Image Processing

Demant, C., Streicher-Abel, B., & Garnica, C. (2013) · Springer eBooks · doi:10.1007/978-3-642-33905-9 · Cited by 52 · see citing works

Automatic identification of spatial defect patterns for semiconductor manufacturing

Wang, C., Wang, S.-R., & Lee, W.-D. (2006) · International Journal of Production Research · doi:10.1080/02772240600610822 · Cited by 48 · see citing works

An Algorithm for Surface Defect Identification of Steel Plates Based on Genetic Algorithm and Extreme Learning Machine

Tian, S., & Xu, K. (2017) · Metals · doi:10.3390/met7080311 · Cited by 48 · see citing works

Convolutional neural network‐based multi‐label classification of PCB defects

Li, Z., Jin, Y., Yang, X., Li, X., Duan, X., Sun, Y., & Liu, H. (2018) · The Journal of Engineering · doi:10.1049/joe.2018.8279 · Cited by 48 · see citing works

Handbook of Machine and Computer Vision

Hornberg, A. (2017) · Wiley eBooks · doi:10.1002/9783527413409 · Cited by 42 · see citing works

Unsupervised Surface Defect Detection Using Deep Autoencoders and Data Augmentation

Mujeeb, A., Dai, W., Erdt, M., & Sourin, A. (2018) · 2018 International Conference on Cyberworlds (CW) · doi:10.1109/cw.2018.00076 · Cited by 40 · see citing works

Modeling the Interactions Among Neighboring Nanostructures for Local Feature Characterization and Defect Detection

Xu, L., & Huang, Q. (2012) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2012.2209417 · Cited by 39 · see citing works

Trends in automated visual inspection

Shirvaikar, M. (2006) · Journal of Real-Time Image Processing · doi:10.1007/s11554-006-0009-6 · Cited by 38 · see citing works

Introduction to Semi-Supervised Learning

Chapelle, O., Schölkopf, B., & Zien, A. (2006) · Cited by 37 · see citing works

Review on Automated Optical (Visual) Inspection and Its Applications in Defect Detection

Lu, R., Ang, 吴. W., Tengda, 张. Z., & Yonghong, 王. W. (2018) · Acta Optica Sinica · doi:10.3788/aos201838.0815002 · Cited by 37 · see citing works

A Neural-Network Approach for Defect Recognition in TFT-LCD Photolithography Process

Chen, L., Su, C., & Chen, M.-H. (2009) · IEEE Transactions on Electronics Packaging Manufacturing · doi:10.1109/tepm.2008.926117 · Cited by 36 · see citing works

Automatic Finger Interruption Detection in Electroluminescence Images of Multicrystalline Solar Cells

Tseng, D.-C., Liu, Y.-S., & Chou, C.-M. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/879675 · Cited by 33 · see citing works

A deep learning-based approach for the automated surface inspection of copper clad laminate images

Zheng, X., Chen, J., Wang, H., Zheng, S., & Kong, Y. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01877-z · Cited by 32 · see citing works

Flatness Defect Detection and Classification in Hot Rolled Steel Strips Using Convolutional Neural Networks

Vannocci, M., Ritacco, A., Castellano, A., Galli, F., Vannucci, M., Iannino, V., & Colla, V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20518-8_19 · Cited by 31 · see citing works

An Automatic Recognition Method for PCB Visual Defects

Zhang, Z., Wang, X., Liu, S., Sun, L., Chen, L., & Guo, Y. (2018) · International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) · doi:10.1109/sdpc.2018.8664974 · Cited by 30 · see citing works

Defect Classification of Printed Circuit Boards based on Transfer Learning

Ghosh, B., Bhuyan, M. K., Sasmal, P., Iwahori, Y., & Gadde, P. (2018) · IEEE Applied Signal Processing Conference (ASPCON) · doi:10.1109/aspcon.2018.8748670 · Cited by 30 · see citing works

Design and evaluation of features and classifiers for OLED panel defect recognition in machine vision

Nguyen, V. H., Pham, V.-H., Cui, X., Ma, M., & Kim, H. (2017) · Journal of Information and Telecommunication · doi:10.1080/24751839.2017.1355717 · Cited by 29 · see citing works

Automatic defect classification of TFT-LCD panels using machine learning

Kang, S., Lee, J. H., Song, K., & Pahk, H.-J. (2009) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2009.5213760 · Cited by 26 · see citing works

Fast Surface Defect Detection Using Improved Gabor Filters

Ma, J., Wang, Y., Chen, S., & Lu, C. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451351 · Cited by 25 · see citing works

Automated surface feature detection using fringe projection: An autoregressive modeling-based approach

Kulkarni, R., Banoth, E., & Pal, P. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.05.014 · Cited by 25 · see citing works

Automated defect detection in textured materials using wavelet-domain hidden Markov models

Hu, G., Zhang, G., & Wang, Q. (2014) · Optical Engineering · doi:10.1117/1.oe.53.9.093107 · Cited by 21 · see citing works

Deep learning based automatic defect classification in through-silicon Via process: FA: Factory automation

Kim, J., Kim, S., Kwon, N., Kang, H., Kim, Y.-D., & Lee, C. (2018) · ASMC proceedings · doi:10.1109/asmc.2018.8373144 · Cited by 21 · see citing works

SMD Defect Classification by Convolution Neural Network and PCB Image Transform

Kim, Y.-G., Lim, D.-U., Ryu, J.-H., & Park, T.-H. (2018) · International Conference on Computing, Communication and Security (ICCCS) · doi:10.1109/cccs.2018.8586818 · Cited by 21 · see citing works

An automatic detection method of mura defects for liquid crystal display using real Gabor filters

Bi, X., Xu, X., & Shen, J. (2015) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2015.7408000 · Cited by 19 · see citing works

A Robust and Fast Deep Learning-Based Method for Defect Classification in Steel Surfaces

Saiz, F. A., Serrano, I., Barandiarán, Í., & Sánchez, J. R. (2018) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2018.8710501 · Cited by 19 · see citing works

Detection and Classification of Defect Patterns in Optical Inspection Using Support Vector Machines

Xie, L., Huang, R., & Cao, Z. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39479-9_45 · Cited by 18 · see citing works

Woven Fabric Defect Detection Based on Convolutional Neural Network for Binary Classification

Gao, C., Zhou, J., Wong, W. K., & Gao, T. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-99695-0_37 · Cited by 18 · see citing works

Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approachPreprint

Li, W., Wang, Z., Li, J., Polson, J., Speier, W. F., & Arnold, C. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.06484 · Cited by 17 · see citing works

A SAR Image Classification Algorithm Based on Multi-Feature Polarimetric Parameters Using FOA and LS-SVM

Luo, S., Sarabandi, K., Tong, L., & Pierce, L. E. (2019) · IEEE Access · doi:10.1109/access.2019.2957547 · Cited by 17 · see citing works

Convolutional Neural Networks Implementations for Computer Vision

Michalski, P., Ruszczak, B., & Tomaszewski, M. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-75025-5_10 · Cited by 15 · see citing works

A deep learning approach to photovoltaic cell defect classification

Banda, P., & Barnard, L. (2018) · Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT) · doi:10.1145/3278681.3278707 · Cited by 15 · see citing works

Inception Dual Network for steel strip defect detection

Liu, Z., Wang, X., & Chen, X. (2019) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2019.8743190 · Cited by 15 · see citing works

A review of SMD-PCB defects and detection algorithms

Hani, A. F. M., Malik, A. S., Kamil, R., & Thong, C.-M. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.920531 · Cited by 13 · see citing works

Product Surface Defect Detection Based on Deep Learning

Lien, P. C., & Zhao, Q. (2018) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/datacom/cyberscitec.2018.00051 · Cited by 13 · see citing works

Supervised texture classification using several features extraction techniques based on ANN and SVM

Ashour, M. W., Hussin, M. F., & Mahar, K. (2008) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2008.4493588 · Cited by 12 · see citing works

An Integrated Deep Neural Network for Defect Detection in Dynamic Textile Textures

Siegmund, D., Prajapati, A., Kirchbuchner, F., & Kuijper, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01132-1_9 · Cited by 11 · see citing works

Recognition of Defects in Steel Surface Image Based on Neural Networks and Morphology

Yang, C., Zhang, J., Ji, G., Fu, Y., & Hong, X. (2007) · Workshop on Digital Media and its Application in Museum & Heritages · doi:10.1109/dmamh.2007.56 · Cited by 7 · see citing works

AUTOMATIC DEFECT CLASSIFICATION OF TFT-LCD PANELS WITH SHAPE, HISTOGRAM AND COLOR FEATURES

Huang, W., & Lu, H. (2013) · International Journal of Image and Graphics · doi:10.1142/s0219467813500113 · Cited by 6 · see citing works

Hybrid QPSO based wavelet neural networks for network anomaly detection

Ma, R., Liu, Y., & Lin, X. (2007) · doi:10.1109/dmamh.2007.69 · Cited by 6 · see citing works

A Defect Inspection Method for Machine Vision Using Defect Probability Image with Deep Convolutional Neural Network

Jang, C., Yun, S., Hwang, H., Shin, H.-M., Kim, S., & Park, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20887-5_9 · Cited by 6 · see citing works

Design of machine vision system for high speed manufacturing environments

Shreya, S., Priya, C. S., & Rajeshware, G. S. (2016) · IEEE India Conference · doi:10.1109/indicon.2016.7838973 · Cited by 5 · see citing works

A Multifaceted Approach for Safety Design and Probabilistic Optimization

Lin, P. T., Manuel, M. C. E., Liu, Y., Chou, Y.-C., Ting, Y., Shyu, S.-S., Chen, C.-K., & Lee, C.-L. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/817820 · Cited by 4 · see citing works

Cad Data-Based Comparison Method For Printed Wiring Board (PWB) Inspection

Sílven, O., Virtanen, I., & Pietikäinen, M. (1985) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.946210 · Cited by 4 · see citing works

Fast defect detection algorithm on the variety surface with random forest using GPUs

Kwon, B. H., & Kang, D. (2011) · Cited by 4 · see citing works

Intelligent Robotic Vision Systems

Gool, L. V., Wambacq, P., & Oosterlinck, A. (2020) · CRC Press eBooks · doi:10.1201/9781003066279-16 · Cited by 2 · see citing works

Steel Surface Defect Recognition: A Survey

Wen, X., Shan, J., He, Y., & Song, K. (2022) · Coatings · doi:10.3390/coatings13010017 · Cited by 191 · see citing works

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Hütten, N., Gomes, M. A., Hölken, F., Andricevic, K., Meyes, R., & Meisen, T. (2024) · Applied System Innovation · doi:10.3390/asi7010011 · Cited by 186 · see citing works

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

Cui, Y., Liu, Z., & Lian, S. (2023) · IEEE Access · doi:10.1109/access.2023.3282993 · Cited by 164 · see citing works

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Prunella, M., Scardigno, R. M., Buongiorno, D., Brunetti, A., Longo, N., Carli, R., Dotoli, M., & Bevilacqua, V. (2023) · IEEE Access · doi:10.1109/access.2023.3271748 · Cited by 162 · see citing works

Deep Learning and Computer Vision Techniques for Enhanced Quality Control in Manufacturing Processes

Islam, M. R., Zamil, M. Z. H., Rayed, M. E., Kabir, M. M., Mridha, M. F., Nishimura, S., & Shin, J. (2024) · IEEE Access · doi:10.1109/access.2024.3453664 · Cited by 158 · see citing works

Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models

Zipfel, J., Verworner, F., Fischer, M., Wieland, U., Kraus, M., & Zschech, P. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109045 · Cited by 127 · see citing works

Computer vision and deep learning-based approaches for detection of food nutrients/nutrition: New insights and advances

Kaushal, S., Tammineni, D. K., Rana, P., Sharma, M., Sridhar, K., & Chen, H. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104408 · Cited by 111 · see citing works

A new lightweight deep neural network for surface scratch detection

Li, W., Zhang, L., Wu, C., Cui, Z., & Niu, C. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10335-8 · Cited by 97 · see citing works

Traffic sign detection based on improved faster R-CNN for autonomous driving

Li, X., Xie, Z., Deng, X. F., Wu, Y., & Pi, Y. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-021-04230-4 · Cited by 85 · see citing works

Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection

Üzen, H., Türkoğlu, M., Aslan, M., & Hanbay, D. (2022) · The Visual Computer · doi:10.1007/s00371-022-02442-0 · Cited by 70 · see citing works

Coating Defects of Lithium-Ion Battery Electrodes and Their Inline Detection and Tracking

Schoo, A., Moschner, R., Hülsmann, J., & Kwade, A. (2023) · Batteries · doi:10.3390/batteries9020111 · Cited by 68 · see citing works

MD-YOLO: Surface Defect Detector for Industrial Complex Environments

Zheng, H., Chen, X., Cheng, H., Du, Y., & Jiang, Z. (2024) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2024.108170 · Cited by 68 · see citing works

SFW-YOLO: A lightweight multi-scale dynamic attention network for weld defect detection in steel bridge inspection

Luo, Y., Ling, J., Wang, J., Zhang, H., Chen, F., Xiao, X., & Lu, N. (2025) · Measurement · doi:10.1016/j.measurement.2025.117608 · Cited by 68 · see citing works

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

de la Rosa, F. L., Sánchez-Reolid, R., Gómez-Sirvent, J. L., Morales, R., & Fernández‐Caballero, A. (2021) · Applied Sciences · doi:10.3390/app11209508 · Cited by 67 · see citing works

A Review of Metal Surface Defect Detection Technologies in Industrial Applications

Qiao, Q., Hu, H., Ahmad, A. A., & Wang, K. (2025) · IEEE Access · doi:10.1109/access.2025.3544578 · Cited by 66 · see citing works

SDD-DETR: Surface Defect Detection for No-Service Aero-Engine Blades With Detection Transformer

Sun, X., Song, K., Wen, X., Wang, Y., & Yan, Y. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3457829 · Cited by 59 · see citing works

Solder Joint Inspection on Printed Circuit Boards: A Survey and a Dataset

Ülger, F., Yüksel, S. E., Yılmaz, A., & Gökcen, D. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3277935 · Cited by 51 · see citing works

Advancements in AI-Driven detection and localisation of solar panel defects

Ghahremani, A., Adams, S. D., Norton, M., Khoo, S., & Kouzani, A. Z. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103104 · Cited by 51 · see citing works

Vision Transformer in Industrial Visual Inspection

Hütten, N., Meyes, R., & Meisen, T. (2022) · Applied Sciences · doi:10.3390/app122311981 · Cited by 50 · see citing works

Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision

Wang, Q., Wang, M., Sun, J., Chen, D., & Shi, P. (2025) · IEEE Access · doi:10.1109/access.2025.3571297 · Cited by 53 · see citing works

A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products

Ibrahim, A. A. M. S., & Tapamo, J. (2024) · Informatics · doi:10.3390/informatics11020025 · Cited by 49 · see citing works

A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

Huang, A. C., Meng, S. H., & Huang, T. J. (2023) · Cluster Computing · doi:10.1007/s10586-023-04115-6 · Cited by 47 · see citing works

Offshore wind turbine tower design and optimization: A review and AI-driven future directions

Ribeiro, J. A., Ribeiro, B. A., Pimenta, F., Tavares, S. M. O., Zhang, J., & Ahmed, F. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126294 · Cited by 44 · see citing works

Zero-shot surface defect recognition with class knowledge graph

Li, Z., Gao, L., Gao, Y., Li, X., & Li, H. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101813 · Cited by 43 · see citing works

Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

Pham, T. T. A., Thoi, D. K. T., Choi, H., & Park, S. (2023) · Sensors · doi:10.3390/s23063246 · Cited by 43 · see citing works

An overview of traditional and advanced methods to detect part defects in additive manufacturing processes

Bhandarkar, V. V., Shahare, H. Y., Mall, A. P., & Tandon, P. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02483-3 · Cited by 43 · see citing works

Research on YOLOv7-based defect detection method for automotive running lights

Chen, J., Bai, S., Wan, G., & Li, Y. (2023) · Systems Science & Control Engineering · doi:10.1080/21642583.2023.2185916 · Cited by 40 · see citing works

Formation mechanism and detection and evaluation methods as well as repair technology of crack damage in fiber-reinforced composite wind turbine blade: a review

Cao, Z., Li, S., Li, C., Li, P., & Ko, T. J. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09230-z · Cited by 38 · see citing works

Transfer learning-based approach using new convolutional neural network classifier for steel surface defects classification

Ibrahim, A. A. M. S., & Tapamo, J. (2024) · Scientific African · doi:10.1016/j.sciaf.2024.e02066 · Cited by 37 · see citing works

Deep learning-based automated steel surface defect segmentation: a comparative experimental study

Sime, D. M., Wang, G., Zeng, Z. P., & Peng, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15307-y · Cited by 33 · see citing works

An Improved YOLOX Model for Detecting Strip Surface Defects

Yi, C., Xu, B., Chen, J., Chen, Q., & Zhang, L. (2022) · steel research international · doi:10.1002/srin.202200505 · Cited by 29 · see citing works

Uncertainty-aware and dynamically-mixed pseudo-labels for semi-supervised defect segmentation

Sime, D. M., Wang, G., Zeng, Z. P., & Peng, B. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103995 · Cited by 28 · see citing works

Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

Li, W., Solihin, M. I., Saruchi, S. A., Astuti, W., Hong, L. W., & Kit, A. C. (2024) · Operations Research Forum · doi:10.1007/s43069-024-00337-5 · Cited by 27 · see citing works

SDDiff: Semi-supervised surface defect detection with Diffusion Probabilistic Model

Wang, X., Li, W., Liu, C., & Ouyang, N. (2024) · Measurement · doi:10.1016/j.measurement.2024.115276 · Cited by 26 · see citing works

Steel surface defect detection method based on improved YOLOv9

Chen, C., Lee, H. L., & Chen, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-10647-1 · Cited by 26 · see citing works

Faster Metallic Surface Defect Detection Using Deep Learning with燙hannel燬huffling

Yasir, S. M., & Ahn, H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.035698 · Cited by 26 · see citing works

Volumetric nondestructive metrology for 3D semiconductor packaging: A review

Su, Y., Shi, J., Hsu, Y.-M., Ji, D.-Y., Suer, A., & Lee, J. (2023) · Measurement · doi:10.1016/j.measurement.2023.114065 · Cited by 25 · see citing works

A novel CNN ensemble framework for bearing surface defects classification based on transfer learning

Ma, J., Liu, M., Hu, S., Fu, J., Gui, C., & Yang, A. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac9c22 · Cited by 25 · see citing works

Lightweight defect detection network based on steel strip raw images

Huang, Y., Chen, Z., Chen, Z., Zhou, D., & Pan, E. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110179 · Cited by 24 · see citing works

DSTEELNet: A Real-Time Parallel Dilated CNN with Atrous Spatial Pyramid Pooling for Detecting and Classifying Defects in Surface Steel Strips

Ragab, K. (2023) · Sensors · doi:10.3390/s23010544 · Cited by 24 · see citing works

Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization

Chen, X., Liu, M., Niu, Y., Wang, X., & Wu, Y. C. (2024) · IEEE Access · doi:10.1109/access.2024.3408718 · Cited by 23 · see citing works

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 · see citing works

Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model

Doss, R., Ramakrishnan, J., Kavitha, S., Ramkumar, S., Latha, G. C. P., & Ramaswamy, K. (2022) · Advances in Materials Science and Engineering · doi:10.1155/2022/1829792 · Cited by 22 · see citing works

Fabric Defect Segmentation System Based on a Lightweight GAN for Industrial Internet of Things

Li, B., Zou, Y., Zhu, R., Yao, W., Wang, J., & Wan, S. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/9680519 · Cited by 22 · see citing works

GelBelt: A Vision-Based Tactile Sensor for Continuous Sensing of Large Surfaces

Mirzaee, M. A., Huang, H.-J., & Yuan, W. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3527306 · Cited by 21 · see citing works

Local–global lightweight ViT model for mini/micro-LED-chip defect recognition

Wei, L., Cai, J., Wen, K., & Zhang, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106247 · Cited by 21 · see citing works

An Attention-Augmented Convolutional Neural Network With Focal Loss for Mixed-Type Wafer Defect Classification

Batool, U., Shapiai, M. I., Mostafa, S. A., & Ibrahim, M. Z. (2023) · IEEE Access · doi:10.1109/access.2023.3321025 · Cited by 21 · see citing works

A Systematic Review on Advancement of Image Segmentation Techniques for Fault Detection Opportunities and Challenges

Rahman, M. M., Rahman, S., Bhatt, S., & Faezipour, M. (2025) · Electronics · doi:10.3390/electronics14050974 · Cited by 20 · see citing works

Machine learning-driven advances in nanotechnology: From materials design to process optimization – A review

Mim, J. J., Mamun, A. A., Nayem, M. H., Mahmud, S., Nath, A., Rahman, S., Fidal, S. A., & Hossain, N. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.114485 · Cited by 20 · see citing works

Steel Strip Quality Assurance With YOLOV7-CSF: A Coordinate Attention and SIoU Fusion Approach

Raj, G. D., & Prabadevi, B. (2023) · IEEE Access · doi:10.1109/access.2023.3333894 · Cited by 20 · see citing works

Category-level selective dual-adversarial network using significance-augmented unsupervised domain adaptation for surface defect detection

Zhang, S., Su, L., Gu, J., Li, K., Wu, W., & PECHT, M. G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121879 · Cited by 19 · see citing works

Few-shot defect recognition for the multi-domain industry via attention embedding and fine-grained feature enhancement

Su, Y., Yan, P., Lin, J., Wen, C., & Fan, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111265 · Cited by 19 · see citing works

A pushing-grasping collaborative method based on deep Q-network algorithm in dual viewpoints

Peng, G., Liao, J., Guan, S., Yang, J., & Li, X. (2022) · Scientific Reports · doi:10.1038/s41598-022-07900-2 · Cited by 18 · see citing works

Moving towards agriculture 4.0: An AI-AOI carrot inspection system with accurate geometric properties

Liong, S., Wu, Y.-L., Liong, G.-B., & Gan, Y. S. (2023) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2023.111632 · Cited by 18 · see citing works

An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants

Matusz-Kalász, D., Bodnár, I., & Jobbágy, M. (2025) · Applied Sciences · doi:10.3390/app15105511 · Cited by 18 · see citing works

Steel Surface Defect Detection Using Learnable Memory Vision Transformer

Ayon, S. T. K., Siraj, F. M., & Uddin, J. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.058361 · Cited by 17 · see citing works

SWRD: A Dataset of Radiographic Image of Seam Weld for Defect Detection

Zhao, X., Wu, J., Zhang, B., Wen, H., Wang, X., Li, Y., & Yu, X. (2025) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-025-01186-w · Cited by 16 · see citing works

A deep residual neural network for semiconductor defect classification in imbalanced scanning electron microscope datasets

de la Rosa, F. L., Gómez-Sirvent, J. L., Morales, R., Sánchez-Reolid, R., & Fernández‐Caballero, A. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109743 · Cited by 16 · see citing works

Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images

Cheng, Y.-C., Hung, Y.-C., Huang, G., Chen, T., Lu, N., Liu, K., & Lin, K.-H. (2024) · Diagnostics · doi:10.3390/diagnostics14232636 · Cited by 16 · see citing works

MSANet: efficient detection of tire defects in radiographic images

Zhao, M., Zheng, Z., Sun, Y., Chang, Y., Tian, C., & Zhang, Y. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac85d1 · Cited by 16 · see citing works

A vision-based hole quality assessment technique for robotic drilling of composite materials using a hybrid classification model

Lee, S., Simeth, A., Hinchy, E. P., Plapper, P., O’Dowd, N. P., & McCarthy, C. T. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-12290-4 · Cited by 15 · see citing works

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 · see citing works

Detecting dyeing machine entanglement anomalies by using time series image analysis and deep learning techniques for dyeing-finishing process

Wang, C.-C., & Kuo, C.-H. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101852 · Cited by 15 · see citing works

Unsupervised fabric defect detection based on multiscale image reconstruction and structural similarity assessment

Yu, Z., Xu, Y., Wang, Y., Wang, Y., & Sheng, X. (2023) · Coloration Technology · doi:10.1111/cote.12744 · Cited by 13 · see citing works

Real-time tunnel lining leakage image semantic segmentation via multiple attention mechanisms

Tan, Y., Li, X., Lai, J., & Ai, J. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad1ddb · Cited by 13 · see citing works

Attentive context and semantic enhancement mechanism for printed circuit board defect detection with two-stage and multi-stage object detectors

Kiobya, T., Zhou, J., Maiseli, B. J., & Khan, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-69207-8 · Cited by 13 · see citing works

Advancing Industrial Object Detection Through Domain Adaptation: A Solution for Industry 5.0

Fatima, Z., Zardari, S., & Tanveer, M. H. (2024) · Actuators · doi:10.3390/act13120513 · Cited by 13 · see citing works

Next-generation Vision Inspection Systems: a pipeline from 3D model to ReCo file

Lupi, F., Freitas, N., Arvana, M., Rocha, A. D., Maffei, A., Barata, J., & Lanzetta, M. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02456-6 · Cited by 12 · see citing works

Integrated Circuit Packaging Defect Analysis and Deep Learning Detection Method

Liu, F., Wang, H., Feng, P., & Zeng, L. (2024) · IEEE Transactions on Components Packaging and Manufacturing Technology · doi:10.1109/tcpmt.2024.3447040 · Cited by 12 · see citing works

OHEH-RTDETR: an improved RT-DETR detection model based on frequency layered processing and advanced feature selection

Wang, H., Zhang, Y., & Wu, W. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adc1f3 · Cited by 12 · see citing works

Recognition of unknown wafer defect via optimal bin embedding technique

Chu, M. S., Park, S., Jeong, J., Joo, K.-H., Lee, Y., & Kang, J. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09447-y · Cited by 12 · see citing works

Defect-Net: a new CNN model for steel surface defect classification

Abbes, W., Elleuch, J. F., & Sellami, D. (2024) · International Symposium on Signal, Image, Video and Communications (ISIVC) · doi:10.1109/isivc61350.2024.10577945 · Cited by 12 · see citing works

DDEG: Addressing data scarcity in additive manufacturing defect detection through joint generation of CT images and defect annotations

Han, R., Wang, Y., Guo, W., Wang, C., Zhang, Y., Zi, Y., & Zhao, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103619 · Cited by 11 · see citing works

Automated visual inspection of manufactured parts using deep convolutional neural networks and transfer learning

Weiher, K., Rieck, S., Pankrath, H., Beuß, F., Geist, M., Sender, J., & Fluegge, W. (2023) · Procedia CIRP · doi:10.1016/j.procir.2023.09.088 · Cited by 11 · see citing works

Input-guidance diffusion model for unknown defect patterns detection in wafer bin map

Moon, S., & Kim, S. B. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103078 · Cited by 11 · see citing works

Surrogate-based optimization framework for enhancing SMT process quality and productivity in electronics manufacturing services

Lee, C., Lin, K.-X., & Chou, C.-W. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-15032-w · Cited by 11 · see citing works

Quantum Deep Learning for Steel Industry Computer Vision Quality Control.

Villalba-Díez, J., Ordieres-Meré, J. B., González‐Marcos, A., & Larzabal, A. S. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.04.216 · Cited by 11 · see citing works

Exploiting CNN’s visual explanations to drive anomaly detection

Fraccaroli, M., Bizzarri, A., Casellati, P., & Lamma, E. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05177-0 · Cited by 10 · see citing works

Machine Learning Techniques for Quality Control in Textile Fabric Manufacturing

Hariharan, V. S., & Sivaraman, K. (2024) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss60660.2024.10625436 · Cited by 10 · see citing works

An ESG-ConvNeXt network for steel surface defect classification based on hybrid attention mechanism

Zhang, N., Liu, Z., Zhang, E., Chen, Y., & Yue, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-88958-6 · Cited by 10 · see citing works

Automated Defect Identification System in Printed Circuit Boards Using Region-Based Convolutional Neural Networks

Weerakkody, K. D., Balasundaram, R. J., Osagie, E. R., & Al-Ani, J. A. (2025) · Electronics · doi:10.3390/electronics14081542 · Cited by 10 · see citing works

ACPP-Net: Enhancing Strip Steel Surface Defect Detection With Efficient Adaptive Convolution and Channel-Spatial Pyramid Pooling

Li, R., Hou, K., Zhu, M., Dai, Q., Ni, J., Liu, X., & Li, X. (2024) · IEEE Access · doi:10.1109/access.2024.3481031 · Cited by 9 · see citing works

A novel context-adaptive multi-scale defect detection method for object surface defects

Zhang, H., Liu, J., Pan, D., Jiang, Z., & Dong, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130927 · Cited by 9 · see citing works

On the precision models of fringe projection profilometry: unification, simplification and connection

Lv, S., Huang, N., Zou, Y., Liu, C., Long, S. Y., Tang, D., Jiang, X., & Kemao, Q. (2026) · Light Science & Applications · doi:10.1038/s41377-026-02300-x · Cited by 9 · see citing works

Semi-supervised auxiliary learning for surface defect detection and segmentation of injection-molded products from small image datasets

Yu, Z., Zhang, Y., Zhang, S., Yi, G., & Qiu, L. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-13275-7 · Cited by 9 · see citing works

An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS

Amini, A., Kanfoud, J., & Gan, T. (2021) · Sensors · doi:10.3390/s21186141 · Cited by 9 · see citing works

Defect Detection of Scroll Fixed Using AI Machine Vision Inspection

Lee, J.-S., Yun, K.-C., & Park, J. K. (2024) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-024-01125-1 · Cited by 8 · see citing works

A deep learning approach to perform defect classification of freeze-dried product

Herve, Q., Ipek, N., Verwaeren, J., & Beer, T. D. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2024.125127 · Cited by 8 · see citing works

3D point cloud analysis for surface quality inspection: A steel parts use case

Ntoulmperis, M., Catti, P., Discepolo, S., van de Kamp, W., Castellini, P., Nikolakis, N., & Alexopoulos, K. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.01.074 · Cited by 8 · see citing works

Systematic Review of Steel Surface Defect Detection Methods on the Open Access Datasets of Severstal and the Northeastern University (NEU)

Aşar, E., & Özgür, A. (2024) · Engineering materials · doi:10.1007/978-3-031-57468-9_3 · Cited by 8 · see citing works

Wafer Surface Semiconductor Defect Classification Using Convolution Neural Network Based Improved Faster R-CNN

Babu, P., Habelalmateen, M. I., Srikanteswara, R., Reddy, R. A., & Purushotham, N. (2024) · International Conference on Data Science and Information System (ICDSIS) · doi:10.1109/icdsis61070.2024.10594305 · Cited by 8 · see citing works

Advances in welding sensing information processing and modelling technology: an overview

Mehta, A., & Vasudev, H. (2024) · Journal of Adhesion Science and Technology · doi:10.1080/01694243.2024.2388141 · Cited by 8 · see citing works

Relationship between the kernel size of a convolutional layer and the optical point spread function in ghost imaging using deep learning for identifying defect locations

Kataoka, S., Mizutani, Y., Uenohara, T., Takaya, Y., & Matoba, O. (2022) · Applied Optics · doi:10.1364/ao.460145 · Cited by 8 · see citing works

Domain Transfer for Surface Defect Detection using Few-Shot Learning on Scarce Data

Gerschner, F., Paul, J., Schmid, L., Barthel, N., Gouromichos, V., Schmid, F. T., Atzmueller, M., & Theissler, A. (2023) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin51400.2023.10217859 · Cited by 8 · see citing works

A Novel Mechanism for Continual Learning based Predictive Quality Inspection in Smart Manufacturing

Nain, G., Pattanaik, K. K., Sharma, G. K., Gauttam, H., & Viriyasitavat, W. (2023) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon58879.2023.10322423 · Cited by 8 · see citing works

Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defects

Cavaliere, G., Lanz, O., Borgianni, Y., & Savio, E. (2024) · Production & Manufacturing Research · doi:10.1080/21693277.2024.2378199 · Cited by 7 · see citing works

Simulation of semiconductor wafer dicing induced faults on chips and their application as augmentation method for a deep learning based visual inspection system

Friedrich, M., Schlosser, T., & Kowerko, D. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02559-0 · Cited by 7 · see citing works

Enhancing IC substrate manufacturing through differential geometry and lightweight networks for etching defect detection

Yu, Y., Huang, D., & Hu, Y. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.04.006 · Cited by 7 · see citing works

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 131 · see citing works

Defect Inspection on Metal Production using image processing

Kamel, O. K., Hassan, M., & Saeid, M. M. (2026) · Statistics Optimization & Information Computing · doi:10.19139/soic-2310-5070-3181

A systematic review of deep learning approaches for surface defect detection in industrial applications

Ameri, R., Hsu, C.-C., & Band, S. S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107717 · Cited by 222 · see citing works

A Review on Industrial Surface Defect Detection Based on Deep Learning Technology

Qi, S., Yang, J., & Zhong, Z. (2020) · International Conference on Machine Learning and Machine Intelligence (MLMI) · doi:10.1145/3426826.3426832 · Cited by 72 · see citing works

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Prunella, M., Scardigno, R. M., Buongiorno, D., Brunetti, A., Longo, N., Carli, R., Dotoli, M., & Bevilacqua, V. (2023) · IEEE Access · doi:10.1109/access.2023.3271748 · Cited by 162 · see citing works