Machine learning-based imaging system for surface defect inspection

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A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

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An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces

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

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A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence

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Automatic classification of pavement crack using deep convolutional neural network

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An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference

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A bearing fault diagnosis model based on CNN with wide convolution kernels

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Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional Autoencoder

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Progress, challenges and trends on vision sensing technologies in automatic/intelligent robotic welding: State-of-the-art review

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Advanced Data Collection and Analysis in Data-Driven Manufacturing Process

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A smart surface inspection system using faster R-CNN in cloud-edge computing environment

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Computer vision and deep learning for fish classification in underwater habitats: A survey

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Prostate Cancer Detection Using Deep Learning and Traditional Techniques

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A Survey on Deep Learning Empowered IoT Applications

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Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN

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A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

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Detection of apple defect using laser-induced light backscattering imaging and convolutional neural network

Wu, A., Zhu, J., & Ren, T. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.106454 · Cited by 113

Mobile-Edge-Computing-Based Hierarchical Machine Learning Tasks Distribution for IIoT

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

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Deep Learning-Based Crack Detection: A Survey

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Surface defect detection of steel strips based on classification priority YOLOv3-dense network

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Improving the Accuracy of Convolutional Neural Networks by Identifying and Removing Outlier Images in Datasets Using t-SNE

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A systematic literature review of defect detection in railways using machine vision-based inspection methods

Kumar, A., & Harsha, S. P. (2024) · International Journal of Transportation Science and Technology · doi:10.1016/j.ijtst.2024.06.006 · Cited by 111

A Novel Method Based on Deep Convolutional Neural Networks for Wafer Semiconductor Surface Defect Inspection

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3D Printed Electronics of Non-contact Ink Writing Techniques: Status and Promise

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Inspecting Method for Defective Casting Products with Convolutional Neural Network (CNN)

Nguyen, T. P., Choi, S.-H., Park, S.-J., Park, S. H., & Yoon, J. (2020) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-020-00197-4 · Cited by 88

Comparative assessment of common pre-trained CNNs for vision-based surface defect detection of machined components

Singh, S. A., Kumar, A. S., & Desai, K. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119623 · Cited by 86

Automated detection of railway defective fasteners based on YOLOv8-FAM and synthetic data using style transfer

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SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection

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Deep learning approach for defective spot welds classification using small and class-imbalanced datasets

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Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN

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Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map

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Computing the characteristics of defects in wooden structures using image processing and CNN

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

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A review of distributed solar forecasting with remote sensing and deep learning

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One-Shot Recognition of Manufacturing Defects in Steel Surfaces

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Deep Learning in Diverse Intelligent Sensor Based Systems

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A systematic review of machine learning approaches in carbon capture applications

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Image surface texture analysis and classification using deep learning

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A Contactless PCBA Defect Detection Method: Convolutional Neural Networks With Thermographic Images

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Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

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Surface defect classification and detection on extruded aluminum profiles using convolutional neural networks

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A Primer on the Factories of the Future

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Convolutional Neural Network Based Surface Inspection System for Non-patterned Welding Defects

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Improving Transactional Data System Based on an Edge Computing–Blockchain–Machine Learning Integrated Framework

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Vision-Based Defect Detection for Mobile Phone Cover Glass using Deep Neural Networks

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

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Deep Convolution Neural Network Approach for Defect Inspection of Textured Surfaces

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Structural damage-causing concrete cracking detection based on a deep-learning method

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Case study: Performance analysis and development of robotized screwing application with integrated vision sensing system for automotive industry

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An Efficient Network for Surface Defect Detection

Lin, Z., Ye, H., Zhan, B., & Huang, X. (2020) · Applied Sciences · doi:10.3390/app10176085 · Cited by 53

LAACNet: Lightweight adaptive activation convolution network-based defect detection on polished metal surfaces

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Intelligent Detection Method of Forgings Defects Detection Based on Improved EfficientNet and Memetic Algorithm

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An accurate detection method for surface defects of complex components based on support vector machine and spreading algorithm

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Rolling Surface Defect Inspection for Drum-Shaped Rollers Based on Deep Learning

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Multi-task convolutional neural network system for license plate recognition

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Strain measurement during tensile testing using deep learning-based digital image correlation

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Generative Adversarial Networks for Road Crack Image Segmentation

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Computer vision defect detection on unseen backgrounds for manufacturing inspection

Mezher, A. M., & Marble, A. E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122749 · Cited by 49

Development of automated feature extraction and convolutional neural network optimization for real-time warping monitoring in 3D printing

Xie, J., Saluja, A., Rahimizadeh, A., & Fayazbakhsh, K. (2022) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2022.2025621 · Cited by 39

PreAugNet: improve data augmentation for industrial defect classification with small-scale training data

Farady, I., Lin, C., & Chang, M. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02109-0 · Cited by 39

MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks

Nguyen, T., Wozencraft, J. M., Taylor, C. J., Kumar, V., Shivakumar, S. S., Miller, I. D., Keller, J. F., Lee, E. S., Zhou, A., Özaslan, T., et al. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2928734 · Cited by 38

An Advanced Operation Mode with Product-Service System Using Lifecycle Big Data and Deep Learning

Ren, S., Zhang, Y., Sakao, T., Liu, Y., & Cai, R. (2021) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-021-00354-3 · Cited by 39

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

Kuric, I., Klarák, J., Bulej, V., Sága, M., Kandera, M., Hajdučík, A., & Tucki, K. (2022) · Applied Sciences · doi:10.3390/app12020864 · Cited by 36

Intelligent detection of edge inconsistency for mechanical workpiece by machine vision with deep learning and variable geometry model

Lin, X., Wang, X., & Li, L. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01641-3 · Cited by 38

Optimization of wire-EDM process parameters for Ni–Ti-Hf shape memory alloy through particle swarm optimization and CNN-based SEM-image classification

Rahul, V. M., Balaji, V. S. S., & Narendranath, S. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101141 · Cited by 35

AI-enabled defect detection in industrial products: A comprehensive survey, key insights and future research challenges

Nahar, L., Awrangjeb, M., & Islam, M. S. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104067 · Cited by 43

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 33

Improved Faster R-CNN algorithm for defect detection in powertrain assembly line

Xu, L., Li, B., Hong, M., & Xingzhong, L. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.04.031 · Cited by 32

Optical inspection of nanoscale structures using a novel machine learning based synthetic image generation algorithm

Purandare, S., Zhu, J., Zhou, R., Popescu, G., Schwing, A. G., & Goddard, L. L. (2019) · Optics Express · doi:10.1364/oe.27.017743 · Cited by 29

Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNN

Zhou, Z., Lu, Q., Wang, Z., & Huang, H. (2019) · Sensors · doi:10.3390/s19225000 · Cited by 29

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective

Kang, S., Jin, R., Deng, X., & Kenett, R. S. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01817-9 · Cited by 28

From data analysis to intelligent maintenance: a survey on visual defect detection in aero-engines

Wu, P., Li, H., Luo, X., Hu, L., Yang, R., & Zeng, N. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/add6c8 · Cited by 29

RoadID: A Dedicated Deep Convolutional Neural Network for Multipavement Distress Detection

Li, Y., Liu, C., Shen, Y., Cao, J., Yu, S., & Du, Y. (2021) · Journal of Transportation Engineering Part B Pavements · doi:10.1061/jpeodx.0000317 · Cited by 27

Application of machine vision-based NDT technology in ceramic surface defect detection – a review

Dong, G., Sun, S., Wang, Z., Wu, N., Huang, P., Feng, H., & Pan, M. (2022) · Materials Testing · doi:10.1515/mt-2021-2012 · Cited by 26

CFE-YOLOv8s: Improved YOLOv8s for Steel Surface Defect Detection

Yang, S., Xie, Y., Wu, J., Huang, W., Yan, H., Wang, J., Bi, W., Yu, X., Wu, Q., & Xie, F. (2024) · Electronics · doi:10.3390/electronics13142771 · Cited by 24

Fruit Image Classification Using Convolutional Neural Networks

Ashraf, S., Kadery, I., Chowdhury, M. A. A., Mahbub, T. Z., & Rahman, R. M. (2019) · International Journal of Software Innovation · doi:10.4018/ijsi.2019100103 · Cited by 24

Strip Steel Defect Classification Using the Improved GAN and EfficientNet

Guan, S., Chang, J., Shi, H., Xiao, X., Li, Z., Wang, X., & Wang, X. (2021) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.1995231 · Cited by 24

Active learning and novel model calibration measurements for automated visual inspection in manufacturing

Rožanec, J. M., Bizjak, L., Trajkova, E., Zajec, P., Keizer, J., Fortuna, B., & Mladenić, D. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02098-0 · Cited by 29

Resistance Welding Spot Defect Detection with Convolutional Neural Networks

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Towards generalizable food source identification: An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis

Chu, Y., Wu, J., Yan, Z., Zhao, Z., Xu, D., & Wu, H. (2024) · Food Research International · doi:10.1016/j.foodres.2024.113967 · Cited by 24

Machine learning-based system for fault detection on anchor rods of cable-stayed power transmission towers

Coutinho, M. S., Novo, L. R. G. S. L., de Melo, M. T., de Medeiros, L. H. A., Barbosa, D. C. P., Alves, M. M., Tarragô, V. L., dos Santos, R. G. M., Neto, H. B. D. T. L., & Gama, P. H. R. P. (2021) · Electric Power Systems Research · doi:10.1016/j.epsr.2021.107106 · Cited by 23

Surface Defect Detection Methods Based on Deep Learning: a Brief Review

Liu, G. (2020) · International Conference on Information Technology and Computer Application (ITCA) · doi:10.1109/itca52113.2020.00049 · Cited by 23

Quality prediction for aluminum diecasting process based on shallow neural network and data feature selection technique

Bak, C., Roy, A. G., & Son, H. (2021) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2021.04.001 · Cited by 27

Automatic detection of surface defects based on deep random chains

Zhang, T., Wang, Z., Li, F., Zhong, H., Hu, X., Zhang, W., Zhang, D., & Liu, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120472 · Cited by 25

A rapid detection method for the surface defects of mosaic ceramic tiles

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