Machine learning-based imaging system for surface defect inspection

Gradient-based learning applied to document recognition

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

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Semantic segmentation supervised deep-learning algorithm for welding-defect detection of new energy batteries

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A Feature Inherited Hierarchical Convolutional Neural Network (FI-HCNN) for Motor Fault Severity Estimation Using Stator Current Signals

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An accurate object detection of wood defects using an improved Faster R-CNN model

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Classification of grazing-incidence small-angle X-ray scattering patterns by convolutional neural network

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Online recognition of magnetic tile defects based on UPM-DenseNet

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Recent advances in continuous nanomanufacturing: focus on machine learning-driven process control

Venkatesan, S., Cullinan, M. A., & Bâldea, M. (2024) · Reviews in Chemical Engineering · doi:10.1515/revce-2024-0029 · Cited by 20

Advanced cover glass defect detection and classification based on multi-DNN model

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Internet of Things ( IoT ) and the Environmental Sustainability: A Literature Review and Recommendations for Future Research

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Hybrid-Learning-Based Operational Visual Quality Inspection for Edge-Computing-Enabled IoT System

Chu, Y., Feng, D., Liu, Z., Zhao, Z., Wang, Z., Xia, X., & Quek, T. Q. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3107902 · Cited by 20

Defect Detection for Metal Shaft Surfaces Based on an Improved YOLOv5 Algorithm and Transfer Learning

Bi, L. H., & Gao, Q. (2023) · Sensors · doi:10.3390/s23073761 · Cited by 24

Contamination Detection Using a Deep Convolutional Neural Network with Safe Machine—Environment Interaction

Hassan, S. A., Khalil, M. A., Auletta, F., Filosa, M., Camboni, D., Menciassi, A., & Oddo, C. M. (2023) · Electronics · doi:10.3390/electronics12204260 · Cited by 18

Deep Learning Based Defect Inspection Using the Intersection Over Minimum Between Search and Abnormal Regions

Choi, E., & Kim, J. (2020) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-019-00269-9 · Cited by 17

Boost-Up Efficiency of Defective Solar Panel Detection With Pre-Trained Attention Recycling

Park, Y., Kim, M. J., Gim, U.-J., & Yi, J. (2023) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2023.3255227 · Cited by 18

Defect classification of composite materials using transfer learning methods

Gulsen, A., Kolukısa, B., Özdemir, A. T., Bakır-Güngör, B., & Güngör, V. Ç. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2422527 · Cited by 17

A Survey on Automatic Printed Circuit Board Defect Detection Techniques

Aggarwal, N., Deshwal, M., & Samant, P. (2022) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite53722.2022.9823872 · Cited by 16

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

Xu, Z., Wu, Z., & Fan, W. (2021) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x21995388 · Cited by 15

A Distributed In-Situ CNN Inference System for IoT Applications

Du, J., Shen, M., & Du, Y. (2020) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd50377.2020.00055 · Cited by 15

Artificial Intelligence in Manufacturing Processes

Chryssolouris, G., Alexopoulos, K., & Arkouli, Z. (2023) · Studies in systems, decision and control · doi:10.1007/978-3-031-21828-6_2 · Cited by 15

Surface Defect Classification in Real-Time Using Convolutional Neural NetworksPreprint

Arikan, S., Varanasi, K., & Stricker, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04671 · Cited by 21

Lateral forces determine dimensional accuracy of the narrow-kerf sawing of wood

Orlowski, K. A., Chuchała, D., Szczepański, M., Migda, W., Wojnicz, W., & Sandak, J. (2022) · Scientific Reports · doi:10.1038/s41598-021-04129-3 · Cited by 14

SSGDD-YOLO: Multiscale Feature Fusion and Multiattention-Based YOLO for Smartphone Screen Glass Defect Detection

Wu, P., Zhou, H., Yu, Y., Miao, Z., Pan, Q., Zhang, X., & Gao, J. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3524584 · Cited by 15

On the Use of Machine Learning for Additive Manufacturing Technology in Industry 4.0

Banad, Y. M. (2019) · Journal of Computer Science and Information Technology · doi:10.15640/jcsit.v7n2a7 · Cited by 14

A Fine-Grained Attention Model for High Accuracy Operational Robot Guidance

Chu, Y., Feng, D., Liu, Z., Zhang, L., Zhao, Z., Wang, Z., Feng, Z., & Xia, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3206388 · Cited by 15

Microstructural inelastic fingerprints and data-rich predictions of plasticity and damage in solids

Papanikolaou, S. (2020) · Computational Mechanics · doi:10.1007/s00466-020-01845-x · Cited by 15

Automatic Damage Detection of Fasteners in Overhaul Processes

Taheritanjani, S., Schoenfeld, R., & Bruegge, B. (2019) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2019.8843049 · Cited by 14

A review of machine vision technology for defect detection in curved ceramic materials

Dong, G., Pan, X., Liu, S., Wu, N., Kong, X., Huang, P., & Wang, Z. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2404497 · Cited by 13

Analysis of the Region of Interest According to CNN Structure in Hierarchical Pattern Surface Inspection Using CAM

Moon, I. Y., Lee, H. W., Kim, S., Oh, Y., Jung, J., & Kang, S.-H. (2021) · Materials · doi:10.3390/ma14092095 · Cited by 13

A Fuzzy Support Vector Machine-Enhanced Convolutional Neural Network for Recognition of Glass Defects

Jin, Y., Zhang, D., Li, M., Wang, Z., & Chen, Y. (2019) · International Journal of Fuzzy Systems · doi:10.1007/s40815-019-00697-9 · Cited by 13

Collaborative deep semi-supervised learning with knowledge distillation for surface defect classification

Manivannan, S. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109766 · Cited by 16

Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

Chaves, D., Saikia, S., Fernández-Robles, L., Alegre, E., & Trujillo, M. (2018) · Revista Iberoamericana de Automática e Informática Industrial RIAI · doi:10.4995/riai.2018.10229 · Cited by 12

Binary classification with ambiguous training data

Otani, N., Otsubo, Y., Koike, T., & Sugiyama, M. (2020) · Machine Learning · doi:10.1007/s10994-020-05915-2 · Cited by 12

A Machine Vision—Based Pipe Leakage Detection System for Automated Power Plant Maintenance

Bao, N., Fan, Y., Ye, Z., & Simeone, A. (2022) · Sensors · doi:10.3390/s22041588 · Cited by 12

SwinTD: Transformer-based detection network for foreign objects in the cut section of tobacco packets

Chen, C., Shen, F., & Dai, C. (2023) · Measurement · doi:10.1016/j.measurement.2023.112953 · Cited by 12

Reduction of Class Activation Uncertainty With Background Information

Kabir, H. M. D. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3570282 · Cited by 27

Application of self-supervised learning in steel surface defect detection

Hu, S., Ma, X., Zhang, Y., & Xu, W. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2025.21 · Cited by 11

Semiconductor wafer surface defect inspection algorithm based on multi-frame differential image summation

Tian, Q., Xiao, S., Duan, Y., Gao, X., & Zhou, W. (2018) · Optical Metrology and Inspection for Industrial Applications · doi:10.1117/12.2502602 · Cited by 11

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

Machine Learning Approach for Defects Identification in Dissimilar Friction Stir Welded Aluminium Alloys AA 7075-AA 1100 Joints

Mishra, A. (2020) · Journal of Aircraft and Spacecraft Technology · doi:10.3844/jastsp.2020.88.95 · Cited by 11

Automated Pavement Crack Detection with Deep Learning Methods: What Are the Main Factors and How to Improve the Performance?

Gong, H., Tešić, J., Tao, J., Luo, X., & Wang, F. (2023) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981231161358 · Cited by 10

Waysides Inspection Using Wayside Processing Imaging and Deep Learning

Thillaikkarasi, R., Yaseen, M. M., Rameshbabu, R., Prabhakaran, R., Kesavan, R., & Jose Anand, A. (2023) · International Conference on Pervasive Computing and Social Networking (ICPCSN) · doi:10.1109/icpcsn58827.2023.00065 · Cited by 10

Visual coating inspection framework via self-labeling and multi-stage deep learning strategies

Han, C., Lee, J., Jun, M. B., Lee, S. W., & Yun, H. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02372-9 · Cited by 10

A deep CNN for Image Analytics in Automated Manufacturing Process Control

Kadar, M., & Onita, D. (2019) · International Conference on Electronics, Computers and Artificial Intelligence (ECAI) · doi:10.1109/ecai46879.2019.9042159 · Cited by 9

Application of Deep Learning for Defect Detection of Paint Film

Xu, F., Liu, Y., Zi, B., & Zheng, L. (2021) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp51882.2021.9408956 · Cited by 10

Towards a FOSS Automatic Classification of Defects for Bridges Structural Health Monitoring

Belcore, E., Pietra, V. D., Grasso, N., Piras, M., Tondolo, F., Savino, P., Polania, D. R., & Osello, A. (2022) · Communications in computer and information science · doi:10.1007/978-3-030-94426-1_22 · Cited by 10

Metal surface defect detection based on improved YOLOv5

Li, Z., Zhang, Y., Fu, X., & Wang, C. (2023) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis58954.2023.10213124 · Cited by 10

Efficient Deep Learning Methods for Identification of Defective Casting Products

Bolla, B. K., Kingam, M., & Ethiraj, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-22405-8_13 · Cited by 11

Outlier Detection in Predictive Analytics for Energy Equipment

Andryushin, A. V., Shcherbatov, I. A., Dolbikova, N. S., Kuznetsova, A., & Tsurikov, G. (2019) · Studies in systems, decision and control · doi:10.1007/978-3-030-32579-4_15 · Cited by 10

A Hierarchical Feature Fusion-based Method for Defect Recognition with a Small Sample

Gao, Y., Gao, L., & Li, X. (2019) · doi:10.1109/ieem44572.2019.8978912 · Cited by 10

Selecting advanced analytics in manufacturing: a decision support model

Lorenz, R., Kraus, M., Wolf, H., Feuerriegel, S., & Netland, T. H. (2022) · Production Planning & Control · doi:10.1080/09537287.2022.2126951 · Cited by 13

Union channel pruning-based U2Net for online surface defect segmentation of aluminum strips in production processes

Lv, Z., Li, Y., Qian, S., Wu, L., & Yang, Y. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02317-8 · Cited by 9

Transfer Learning-Based Defect Detection System on Wafer Surfaces

Chu, S.-L., Su, E., & Ho, C.-C. (2025) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2025.3532897 · Cited by 9

Pixel-level segmentation of spalling in masonry structures based on deep learning

Wang, T., Zhang, J., Chen, W., Chun, Q., & Sun, J. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.120686 · Cited by 8

Defect inspection of optical components based on dark-field polarization imaging and convolutional neural network recognition algorithms

Xu, C., Zheng, D., Huang, Y., & Zeng, Z. (2023) · Optical Engineering · doi:10.1117/1.oe.62.4.043101 · Cited by 11

Machine learning-based processing of unbalanced data sets for computer algorithms

Zhou, Q., Qi, Y., Tang, H., & Wu, P. (2023) · Open Computer Science · doi:10.1515/comp-2022-0273 · Cited by 9

Research on surface defect detection system and method of train bearing cylindrical roller based on surface scanning

Bao, Y., Zhou, Z., Wei, S., & Xiang, J. (2023) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-023-0808-8 · Cited by 9

Low-Resolution Steel Surface Defects Classification Network Based on Autocorrelation Semantic Enhancement

Guo, X., Gong, K., & Lu, C. (2023) · Coatings · doi:10.3390/coatings13122015 · Cited by 9

Assembly Defect Detection of Atomizers Based on Machine Vision

Wang, J., Hu, H., Chen, L., & He, C. (2019) · International Conference on Automation, Control and Robotics Engineering (CACRE) · doi:10.1145/3351917.3351925 · Cited by 9

Complex technical system condition diagnostics and prediction computerization

Rudnichenko, N. D., Vychuzhanin, V., Polyvianchuk, A., & Mateichyk, V. (2020) · doi:10.32782/cmis/2608-4 · Cited by 9

Improved Method Based on Faster R-CNN Network Optimization for Small Target Surface Defects Detection of Aluminum Profile

Yang, Y., Sun, Q., Zhang, D., Shao, L., Song, X., & Li, X. (2021) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi52946.2021.9679509 · Cited by 9

Assessment of milling condition by image processing of the produced surfaces

Carbone, N., Bernini, L., Albertelli, P., & Monno, M. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10516-5 · Cited by 10

A defect detection system of glass tube yarn based on machine vision

Bao, J., Jing, J., & Xie, Y. (2023) · Journal of Industrial Textiles · doi:10.1177/15280837231152878 · Cited by 9

Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

Liu, H., Zhang, C., & Huang, D. (2017) · Journal of Electrical and Computer Engineering · doi:10.1155/2017/7406568 · Cited by 9

Augmented Hybrid Learning for Visual Defect Inspection in Real-World Hydrogen Storage Manufacturing Scenarios

Chu, Y., He, Y., Xiong, X., Lou, Y., Yu, C., & Duan, L. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3370228 · Cited by 8

Development of cost-effective IoT module-based pipe classification system for flexible manufacturing system of painting process of high-pressure pipe

Yoo, Y., & Cho, K.-S. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-08478-1 · Cited by 8

Deep Learning-Based Models for Porosity Measurement in Thermal Barrier Coating Images

Lu, Y., Chen, W.-B., Wang, X., Ailsworth, Z., Tsui, M., Al-Ghaib, H. A., & Zimmerman, B. (2020) · International Journal of Multimedia Data Engineering and Management · doi:10.4018/ijmdem.2020070102 · Cited by 8

U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion IdentificationPreprint

Nguyen, T., Özaslan, T., Miller, I. D., Keller, J. F., Loianno, G., Taylor, C. J., Lee, D. D., Kumar, V., Harwood, J., & Wozencraft, J. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06576 · Cited by 8

Defect Detection of Metal Nuts Applying Convolutional Neural Networks

Sauter, D., Schmitz, A., Dikici, F., Baumgartl, H., & Buettner, R. (2021) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac51774.2021.00043 · Cited by 7

Security in defect detection: A new one-pixel attack for fooling DNNs

Wang, P., Li, Q., Li, D., Meng, S., Bilal, M., & Mukherjee, A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101689 · Cited by 7

Defect inspection in semiconductor images using FAST-MCD method and neural network

Yu, J., Han, S., & Lee, C.-O. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-12287-z · Cited by 8

Research on Detection Method of Sheet Surface Defects Based on Machine Vision

Huang, Y., Yi, M., Yang, W., & Yang, M. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/632/5/052085 · Cited by 7

Automated Surface Defects Detection on Mirrorlike Materials by using Faster R-CNN

Karangwa, J., Kong, L., You, T., & Zheng, J. (2020) · International Conference on Information Science and Control Engineering · doi:10.1109/icisce50968.2020.00341 · 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

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov, I. A., Jovančević, I., Mokhtari, N. I., & Orteu, J. (2020) · Journal of Electronic Imaging · doi:10.1117/1.jei.29.4.041008 · Cited by 7

Hybrid Semiconductor Wafer Inspection Framework via Autonomous Data Annotation

Han, C., Chun, H., Lee, J., Zhou, F., Yun, H., Lee, C., & Jun, M. B. (2024) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4065276 · Cited by 6

Bayesian Model Updating of Multiscale Simulations Informing Corrosion Prognostics Using Conditional Invertible Neural Networks

Qian, G., Zeng, J., Hu, Z., & Todd, M. D. (2024) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · doi:10.1115/1.4065845 · Cited by 6

A Comprehensive Review of Challenges Using AI for Smart Manufacturing

Sharma, A., Sim, K. Y., & Chandrasekaran, S. (2025) · International Conference on Computer and Automation Engineering · doi:10.1109/iccae64891.2025.10980576 · Cited by 6

Creation of Predictive Analytics System for Power Energy Objects

Аракелян, Э. К., Pashchenko, A. F., Shcherbatov, I. A., Tsurikov, G., & Titov, F. (2019) · International Conference Management of Large-Scale System Development (MLSD) · doi:10.1109/mlsd.2019.8911082 · Cited by 6

Enhancing Clustering Algorithm with Initial Centroids in Tool Wear Region Recognition

Kasim, N. A., Nuawi, M. Z., Ghani, J. A., Rizal, M., Ngatiman, N. A., & Haron, C. H. C. (2021) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-020-00450-5 · Cited by 6

Learning deep domain-agnostic features from synthetic renders for industrial visual inspection

Abubakr, A. G., Jovančević, I., Mokhtari, N., Abdallah, H. B., & Orteu, J. (2022) · J. Electronic Imaging · doi:10.1117/1.jei.31.5.051604 · Cited by 6

Deeper and Mixed Supervision for Salient Object Detection in Automated Surface Inspection

Yan, S., Song, X., & Liu, G. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/3751053 · Cited by 6

Aluminium metal matrix composite casting defect detection using deep convolution neural networks

Natrayan, L. (2025) · Journal of the Australian Ceramic Society · doi:10.1007/s41779-025-01322-9 · Cited by 5

A Comparative Analysis of Oak Wood Defect Detection Using Two Deep Learning (DL)-Based Software

Jambreković, B., Veselčić, F., Ištok, I., Sinković, T., Živković, V., & Sedlar, T. (2024) · Applied System Innovation · doi:10.3390/asi7020030 · Cited by 5

Classification with Convolutional Neural Networks in MapReduce

Chen, M. (2024) · Journal of Computer and Communications · doi:10.4236/jcc.2024.128011 · Cited by 7

A deep-learning-based approach for automated wagon component inspection

Rocha, R. D. L., Siravenha, A. C., Gomes, A. C., Serejo, G. L., Silva, A. F. B., Rodrigues, L. M., Braga, J., Dias, G., Carvalho, S. R., & de Souza, C. R. B. (2018) · ACM Symposium on Applied Computing · doi:10.1145/3167132.3167157 · Cited by 7

Research on defect detection of improved target detection algorithm on the image surface of 5G communication ring

Zhu, H. (2022) · Advances in Complex Systems · doi:10.1142/s1793962323410118 · Cited by 5

Transferable Learning Architecture for Scalable Visual Quality Inspection

Talha, M., Rashid, S. F., Iftikhar, Z., Afzal, M. T., & Liu, Y. (2022) · doi:10.1109/icai55435.2022.9773637 · Cited by 5

Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience

Mukherjee, A., Glatt, M. F., Mustafa, W. A. A., Kloft, M., & Aurich, J. C. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.10.054 · Cited by 5

Anomaly Detecting Geometric Transformation Network with Outlier Exposure Defect Inspection of Real Industrial Data

Kwon, Y., & Kang, D. (2022) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-022-00736-w · Cited by 5

An Unsupervised Anomaly Detection Scheme based on Memory Mechanism in Cloud-edge Collaboration Scenario

Zhu, L., Zhang, J., Li, Y., Xu, X., Li, Y., Zhao, C., & Wang, W. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10055281 · Cited by 5

Vision System for Smart Manufacturing: A Review

Sharma, A., & Kulkarni, A. (2023) · IEEE Engineering Informatics · doi:10.1109/ieeeconf58110.2023.10520579 · Cited by 4

Overview of Silicon Carbide Wafer Defect Types and Inspection Techniques

Liu, Y.-C., Chen, W.-J., Qi, X., Shen, Y., Gu, L., & Ma, H.-P. (2024) · China International Forum on Solid State Lighting & International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) · doi:10.1109/sslchinaifws64644.2024.10835376 · Cited by 4

Enhancing Distributed In-Situ CNN Inference in the Internet of Things

Du, J., Du, Y., Huang, D., Lu, Y., & Liao, X. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176408 · Cited by 4

Applications of LPWANs

Khan, M. A., Anjum, M., Hassan, S. A., & Jung, H. (2023) · Springer eBooks · doi:10.1007/978-3-031-32935-7_6 · Cited by 4

A fast detection method for building wood measurement based on interval type-2 fuzzy model

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Detecting and classifying defects on the surface of water heaters: Development of an automated system

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