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

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

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

Deep learning

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

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 73,472

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

Support-vector networks

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

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

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

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

Review of Research on Ceramic Surface Defect Detection Based on Deep Learning

Wang, Y., Zhang, L., Zhao, X., Tang, B., & Yang, W. (2025) · Electronics · doi:10.3390/electronics14122365 · Cited by 7

Dual-path segmentation network for automatic fabric defect detection

Yu, Z., Xu, Y., Wang, Y., Sheng, X., & Xie, G. (2023) · Textile Research Journal · doi:10.1177/00405175231192057 · Cited by 8

Automated catastrophic optical damage inspection of semiconductor laser chip based on multi-scale strip convolution aggregation

Guo, S., Li, D., Zhao, J., Jia, H., Luo, B., Bao, T., & Lv, Y. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02079-y · Cited by 7

Convolutional Attention Network with edge supervision for void and spot segmentation in chip resistors based on X-ray images

Zhang, M., Chen, M., & Cao, X. (2024) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2024.108169 · Cited by 7

Aluminium foil micro-defect detection method based on motion-induced eddy current using differential TMR sensor spatial position compensation

Wei, B., Ma, Q., Wen, S., Zhang, J., Peng, L., & Huang, S. (2025) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2456674 · Cited by 7

A new machine vision–based intelligent detection method for gear grinding burn

Dong, L., Chen, W., Yang, S., & Yu, H. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11021-z · Cited by 7

Multi-class classification of paint/coating defects using transfer learning

Dhrangdhariya, P., Saini, P., Maiti, S., & Rai, B. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111320 · Cited by 6

Deep Learning Algorithms for Defect Detection on Electronic Assemblies: A Systematic Literature Review

Magaña, B. M., Hernández-Uribe, Ó., Cárdenas-Robledo, L. A., & Cantoral-Ceballos, J. A. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make8010005 · Cited by 6

Surface Defect Detection with Limited Training Data: A Case Study on Crown Wheel Surface Inspection

Zhu, X., Björkman, M., Maki, A., Hanson, L. Å., & Mårtensson, P. (2023) · Procedia CIRP · doi:10.1016/j.procir.2023.09.172 · Cited by 6

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

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

YOLOv5-S-G-B: a lightweight intelligent detection model for cardboard surface defects

Yang, M., Li, D., Luo, P., Wan, X., Zhang, Z., Zhou, L., & Jia, Y. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03369-w · Cited by 6

Conditional Domain Confusion Networks for Cross-Domain Detection

Hu, H., Ji, X., Chi, S., & Jiao, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3203453 · Cited by 6

Integrating explainable artificial intelligence with vision-based surface defect detection systems for realizing zero-defect manufacturing

Singh, S. A., & Desai, K. A. (2025) · International Journal of Production Research · doi:10.1080/00207543.2025.2605085 · Cited by 5

LearnMat: Semantic-Aware Self-Supervision Fine-Grained Visual Recognition

Li, S., Cai, Q., Zhang, F. W., Shu, Y., Li, G., Dong, J., Liu, L., & Zhang, D. D. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3671661 · Cited by 5

Harnessing Artificial Intelligence for Optimum Performance in Industrial Automation

Khan, T. A., Ali, S. M., Ali, K. M., Aziz, A., Ahmad, S., Anwar, A., & Khan, S. A. (2025) · International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP) · doi:10.3390/engproc2024076105 · Cited by 5

ISP-AD: a large-scale real-world dataset for advancing industrial anomaly detection with synthetic and real defects

Krassnig, P. J., & Gruber, D. P. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02778-z · Cited by 6

Noise-robust deep learning ghost imaging using a non-overlapping pattern for defect position mapping

Kataoka, S., Mizutani, Y., Uenohara, T., Takaya, Y., & Matoba, O. (2022) · Applied Optics · doi:10.1364/ao.470770 · Cited by 5

A Novel Learning Based Non-Lambertian Photometric Stereo Method for Pixel-Level Normal Reconstruction of Polished Surfaces

Cao, Y., Wei, X., Liu, W., Ding, B., Yang, J., & Cao, Y. (2022) · Machines · doi:10.3390/machines10020120 · Cited by 5

An improved cross-dimensional collaborative bridge defect detection model based on UAV images

Ni, J., Ji, Q., Zhao, Y., Cao, W., & Shi, P. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae09c0 · Cited by 4

RLA-YOLO: A lightweight and accurate method for real-time steel plate defect detection

Meng, L., Lei, R., Song, Z., Cai, Q., Zhu, Y., & Liu, H. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105700 · Cited by 4

A novel deep learning method for on-line monitoring of grate bar defects in sintering machine system

Zou, Y., Xiang, J., Han, M., Zhai, L., Wu, Y., Zhou, F., Yan, G., & Zhao, L. (2023) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2023.100544 · Cited by 4

AFCN: An attention‐directed feature‐fusion ConvNeXt network for low‐voltage apparatus assembly quality inspection

Guo, H., Bao, Y., Hu, S., Luan, C., Fu, J., Li, L., Zhang, Y., Sun, Y., & Nie, Z. (2024) · IET Image Processing · doi:10.1049/ipr2.13085 · Cited by 4

Multi-level joint distributed alignment-based domain adaptation for cross-scenario strip defect recognition

Liu, K., Yang, Y. Z., Yang, X., Wang, J., Liu, W., & Chen, H. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02344-z · Cited by 4

Enhancing Object Detection Performance for Small Objects Through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios

Antony, J., Hegiste, V., Nazeri, A., Ghinani, H. T., Walunj, S., Plociennik, C., & Ruskowski, M. (2024) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-57496-2_10 · Cited by 4

A Bank Card Number Recognition Method Based on Convolutional Neural Network

Shuiqi, C., Guo, Y., & Chen, Y. (2021) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce52179.2021.9445952 · Cited by 4

Accuracy assessment of semantic segmentation for automatic aesthetic control on composite components

D’Emilia, G., Silvestri, A. D., Gaspari, A., & Natale, E. (2022) · Measurement · doi:10.1016/j.measurement.2022.110778 · Cited by 4

Development of a deep learning machining feature recognition network for recognition of four pilot machining features

Mohammadi, N., & Nategh, M. J. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09839-0 · Cited by 4

Research on defect detection of toy sets based on an improved U-Net

Yang, D., Chen, N., Tang, Q., Zhang, H., & Liu, J. (2023) · The Visual Computer · doi:10.1007/s00371-023-02834-w · Cited by 4

Deep learning-based sustainable subsurface anomaly detection in Barker-coded thermal wave imaging

Parvez, M., Mohammad, A. B., Ghali, V. S., Yadav, G. V. P. C. S., Vesala, G. T., Lakshmi, A. V., Alagarsamy, A., Palanisamy, S., Kechagias, J. D., & Santulli, C. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11753-y · Cited by 4

HMNet: High-Resolution Mamba Network for Semantic Segmentation of Surface Defects

Xiao, H., Ren, Z., & Cai, S. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033237 · Cited by 3

YOLO-MAD: Multi-Scale Geometric Structure Feature Extraction and Fusion for Steel Surface Defect Detection

Ding, H., Chen, J., Ye, H., & Chen, Y. (2025) · Applied Sciences · doi:10.3390/app15147887 · Cited by 4

Label-Efficient PCB Defect Detection with an ECA–DCN-Lite–BiFPN–CARAFE-Enhanced YOLOv5 and Single-Stage Semi-Supervision

Wang, Z., Ramli, N., & Thio, T. H. G. (2025) · Sensors · doi:10.3390/s25237283 · Cited by 3

Assessing quality risk scores using explainable artificial intelligence for advanced vision inspection

Baek, J., Seo, J., Yu, E., & Joung, J. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02751-w · Cited by 3

Accurate detection of photovoltaic panel defects via visible-infrared image fusion and a lightweight enhanced YOLOv11n model

Zhang, Z., Chen, D., Yang, J., Li, J., Xiao, H., & Liu, Q. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae3457 · Cited by 3

ATL-DETR: an improved RT-DETR model for steel surface defect detection

Xiong, Z., Jun, W., & Wang, J. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae85be · Cited by 3

Design and fabrication of a high-efficiency defect inspection prototype for diary plastic cutlery based on machine-vision with improved deep leaning algorithm

Yang, J. J., Qin, Y., Zhu, Z., Xu, X., & Guan, D. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19395-2 · Cited by 3

Wafer defect detection based on improved YOLOv8

Diao, J., Zou, L., & Zhang, G. Q. (2024) · International Conference on Electronic Information Engineering and Data Processing (EIEDP) · doi:10.1117/12.3033091 · Cited by 3

A systematic quality-integrated diagnostic method for complex product assembly using multi-task spatial–temporal transfer learning

Cheng, X., Huang, F., & Qiu, L. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14433-7 · Cited by 3

Mobile Web App for the Digitization and Annotation of Manual Visual Inspection Tasks

Koch, J., Jevremovic, D., & Schüppstuhl, T. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.08.373 · Cited by 3

Deep Learning-Based Decision Support System for Automatic Detection and Grading of Surface Corrosion on Galvanized Steel Sheets

Özdemir, M. E., & Karakuş, F. (2024) · Electronics · doi:10.3390/electronics13203998 · Cited by 3

Steel Surface Defect Detection and Classification Using Bag of Visual Words with BRISK

Ibrahim, A. A. M. S., & Tapamo, J. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-2468-4_18 · Cited by 3

BlurRes-UNet: A novel neural network for automated surface characterisation in metrology

Cui, W., Lou, S., Zeng, W., Kadirkamanathan, V., Qin, Y., Scott, P. J., & Jiang, X. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104228 · Cited by 3

Real-time machine learning for in situ quality control in hybrid manufacturing: a data-driven approach

Mavaluru, D., Tipparti, A., Tipparti, A. K., Ameenuddin, M., Ramakrishnan, J., & Samrin, R. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-15008-w · Cited by 3

Industrial robot product inspection system based on 3D vision guidance

Xing, B. (2025) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-025-00449-z · Cited by 3

The study on surface defect detection of stamped parts based on improved deep learning

Xia, T., Zhou, L., & Quan, L. (2025) · Mechanics & Industry · doi:10.1051/meca/2025019 · Cited by 2

A multi-threshold image segmentation method for mechanical parts based on strategy-improved optimization

Yuan, X., Yao, L., Zhang, T., Li, G., & Chen, Q. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122807 · Cited by 2

LightDefectNet: A lightweight end-to-end model for multiscale defects and complex backgrounds in cold-rolled steel

Wang, X., Pu, Y., Zhao, X., Peng, W., & Sun, J. (2026) · Computers in Industry · doi:10.1016/j.compind.2026.104464 · Cited by 2

Surface flaw detection of small automotive component based on improved faster R-CNN and YOLO algorithms

Li, W., Heltha, F., Rahman, A., & Solihin, M. I. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18208-0 · Cited by 3

Towards semi-supervised and unsupervised source verification of additively manufactured parts with sparse data

Bimrose, M. V., Zheng, W., Tawfick, S., & King, W. P. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.06.016 · Cited by 2

Deep Learning-Based Automated Industrial Surface Defect Classification

Alrayes, R., & Rahman, A. (2026) · Computers · doi:10.3390/computers15070417 · Cited by 2

The good, the bad and the ugly in artificial intelligence

Mery, D., Arriagada-Bruneau, G., & Dunstan, J. (2026) · AI and Ethics · doi:10.1007/s43681-026-01200-5 · Cited by 2

Simultaneous Measurement of Conductivity and Thickness Using Motion-Induced Eddy Current With a One-Magnet–Two-Sensor Configuration

Wei, B., Peng, L., Feng, B., Wang, R., Li, N., Li, S., & Huang, S. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3709407 · Cited by 2

Visual Inspection of Steel Surface Defects Based on Improved Auxiliary Classification Generation Adversarial Network

Li, K., Yang, Q. Z., Lei, S., Gu, J., & Wensheng, S. (2022) · Journal of Mechanical Engineering · doi:10.3901/jme.2022.24.032 · Cited by 2

Digital Computer Vision Technologies for Safety Management in Steel Manufacturing

Lan, R., Awolusi, I., & Cai, J. (2023) · Iron & Steel Technology Conference and Exposition (AISTech) · doi:10.33313/387/002 · Cited by 2

Classification of Surface Defects on Steel Sheet Images Using DenseNet121 Architecture

Do, T.-L., Nguyen, T.-G., Nguyen, K.-Q., Nguyen, T.-N., & Nguyen, N.-N. (2023) · Lecture notes in civil engineering · doi:10.1007/978-981-99-7434-4_74 · Cited by 2

Improving the reliability of deep learning computational ghost imaging with prediction uncertainty based on neighborhood feature maps

Kataoka, S., Mizutani, Y., Uenohara, T., Ipus, E., Nitta, K., Matoba, O., Takaya, Y., & Tajahuerce, E. (2024) · Applied Optics · doi:10.1364/ao.511817 · Cited by 2

Assessing and Recommending Brand Features of Electronic Products through Online Reviews and BiLSTM Models

Lou, P. (2024) · IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) · doi:10.1109/icsece61636.2024.10729421 · Cited by 2

Towards low-cost surface inspection using artificial vision in practical industrial environments

Usamentiaga, R., Menendez, F., Pérez, L., & Elvira, P. (2025) · IEEE Industry Applications Society Annual Meeting (IAS) · doi:10.1109/ias62731.2025.11061523 · Cited by 2

A Novel Method to Inspect 3D Ball Joint Socket Products Using 2D Convolutional Neural Network with Spatial and Channel Attention

Mustafaev, B., Tursunov, A., Kim, S., & Kim, E. S. (2022) · Sensors · doi:10.3390/s22114192 · Cited by 2

A Novel Method to Inspect 3D Ball Joint Socket Products Using 2D Convolutional Neural Network with Spatial and Channel Attention

Mustafaev, B., & Kim, E. S. (2022) · Vide Leaf, Hyderabad eBooks · doi:10.37247/pasens2ed.2.22.28 · Cited by 2

Automatic Examination of Condition of Used Books with YOLO-Based Object Detection Framework

Hong, S., & Jeong, J.-W. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.038319 · Cited by 2

Çift Doğrusal CNN Kullanarak Çelik Yüzey Kusurlarının Sınıflandırılması

Güçlü, E., Aydın, İ., & Akın, E. (2024) · Fırat Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.35234/fumbd.1323339 · Cited by 2

On the Performance of Deep Learning in the Full Edge and the Full Cloud Architectures

Benbarrad, T., Salhaoui, M., & Arioua, M. (2021) · International Conference on Networking, Information Systems & Security (NISS) · doi:10.1145/3454127.3457632 · Cited by 2

ESW-YOLO: A lightweight YOLO model for defect detection in bottled liquorPreprint

Wang, X., Lan, X., & Liu, L. (2024) · Research Square · doi:10.21203/rs.3.rs-5369988/v1 · Cited by 2

A Metal Surface Defect Detection Algorithm Based on Mixed Supervised and Cross Stage Partial Darknet

Zong, R., Liu, Q., Wang, J., Jia, X., Qin, N., & Huang, D. (2024) · Chinese Control Conference (CCC) · doi:10.23919/ccc63176.2024.10661652 · Cited by 1

Exploring the Dynamics of XR and AI Synergy in Architectural Design

Rodriguez, J. D. S., & Joyce, S. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76803-3_11 · Cited by 1

mmHand: Toward Pixel-Level-Accuracy Hand Localization Using a Single Commodity mmWave Device

Zhang, X., Li, Z., Xu, C., Song, L., Li, H., Xue, H., Wu, Y., & Xu, W. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3546560 · Cited by 1

Microsurface Defect Recognition via Microlaser Line Projection and Affine Moment Invariants

Rodríguez, J. A. M. (2025) · Coatings · doi:10.3390/coatings15040385 · Cited by 1

Truncated and integrated class activation maps for weakly supervised defect detection

Dong, H., Liu, B., Dong, Y., & Liu, G. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adcf3d · Cited by 1

Research progress on key technologies for product surface defect detection based on empirical mode decomposition and deep learning

Xiong, J., Zhang, Q., Wang, Q., Yang, J., Dong, X., & Liang, Q. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16431-9 · Cited by 1

Design of Image Semantic Communication System Based on Deep Learning

Li, J., Lin, B., Han, X., Mei, X., Xiong, W., Hu, Y., & Lyv, F. (2025) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc65529.2025.11148867 · Cited by 1

Design and Evaluation of a Laser Triangulation System for Pencil Lead Defect Inspection

Almtireen, N., Kurik, K., Ryalat, M., & Schubert, D. (2025) · Applied System Innovation · doi:10.3390/asi8060184 · Cited by 1

Subjective Evaluation of Operator Responses for Mobile Defect Identification in Remanufacturing: Application of NLP and Disagreement Tagging

Ahmed, A., Mohandas, R., Joorabchi, A., & Hayes, M. J. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9120312 · Cited by 1

A DINOv2-based self-supervised learning framework for automated detection of surface defects in injection molding

Pieressa, A., Lin, C., Lucchetta, G., & Turng, L. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-17386-1 · Cited by 1

ANN-based parameter selection for tolerance-compliant FDM manufacturing: A drawing-driven optimization approach

Sadaoui, S. E., Lourari, A. W., Habbouche, H., & Hamdaoui, N. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18808-w · Cited by 1

Visual defect inspection of touch screens using multi-angle filtering in curvelet domain

Lin, H.-D., Li, J.-M., & Lin, C.-H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e33607 · Cited by 1

Evaluation of Electrical Properties and Uniformity of Single Wall Carbon Nanotube Dip-Coated Conductive Fabrics Using Convolutional Neural Network-Based Image Analysis

Kim, E., Kim, S.-U., & Kim, J. (2024) · Processes · doi:10.3390/pr12112534 · Cited by 1

Hyperbolic geometry enhanced feature filtering network for industrial anomaly detection

Feng, Y., Liu, J., & Gai, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-07550-0 · Cited by 1

Defect Classification of Additive Manufacturing Product using Deep Learning Method

Kamarulzaman, N. N., Damanhuri, N. S., Salleh, N. A. M., Othman, N. A., Meng, B. C. C., & Handayani, A. N. (2025) · IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) · doi:10.1109/i2cacis65476.2025.11100369 · Cited by 1

Deep Learning Approaches for Identifying Defective Silicon Wafers

Sneha, A., & Vijayaraghavan, V. (2025) · IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies (INSPECT) · doi:10.1109/inspect67393.2025.11350598 · Cited by 1

A YOLOv8-Based Network for Surface Defect Detection in Strip Steel

Jiang, J., Su, Y., & Song, J. (2026) · Journal of Computer and Communications · doi:10.4236/jcc.2026.142006 · Cited by 1

FAD-Enhanced Visual Tracking for Textile Defect Detection: A Multispectral Feature Fusion Ap-proach

Wei, H., & Hu, Z. (2026) · Textile & Leather Review · doi:10.31881/tlr.2026.587 · Cited by 1

A deformable convolution and shuffle attention enhanced network for surface defect detection

Tang, Y., Lv, X., & Sun, T. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114864 · Cited by 1

Hybrid CNN–Transformer–GRU framework with improved residual shrinkage network and DCCS optimisation for steel surface defect classification

Kumar, B., Singh, S. K., Kumar, S., & Sinha, B. B. (2026) · Australian Journal of Mechanical Engineering · doi:10.1080/14484846.2026.2675149 · Cited by 1

Exemplar-based comparative learning for visual anomaly detection under high feature diversity and spatial entropy

Zhan, Q., & Li, C.-H. G. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18573-w · Cited by 1

Automatic Labeling in Image Segmentation and Classification for TFT-LCD Manufacturing

Liu, C., Vaassen, S., Manoj, L., Zhan, X. Q., Chen, X., Ajay, S. R., Lu, Z. Y., Wittstamm, M., Jain, S., Zhang, C., et al. (2022) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma54519.2022.9856233 · Cited by 1

Welding Groove Edge Detection Method Using Lightweight Fusion Model Based on Transfer Learning

Guo, B., Rao, L., Li, X., Li, Y., Yang, W. T., & Li, J. (2023) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800142351014x · Cited by 1

Research on Defect Detection of Electronic Components in Facility Greenhouse Based on Improved YOLOv5

Qi, K., Yang, Z. Z., Liang, Z., Fan, Y., Xu, H., Wu, Y., Wang, B., Cui, Y., & Wang, S. (2023) · IEEE Access · doi:10.1109/access.2023.3336426 · Cited by 1

Structure damage diagnosis of bleacher based on DSKNet model

Cai, C., Guo, X., Xue, Y., & Ren, J. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05834-8 · Cited by 1

In-situ quality inspection system of injection parts based on transfer learning

Yang, W., Shan, S., Jin, M., Liu, Y., Zhang, Y., & Li, D. (2024) · Robotic Intelligence and Automation · doi:10.1108/ria-10-2023-0143 · Cited by 1

Characterization of conductive particle dispersion in textile coatings through Joule’s effect monitoring analysis

Ruiz-Calleja, T., Jiménez‐Suárez, A., Calderón‐Villajos, R., & Prolongo, S. G. (2024) · Textile Research Journal · doi:10.1177/00405175241235652 · Cited by 1

Detection Method of Stator Coating Quality of Flat Wire Motor Based on Improved YOLOv8s

Wang, H., Chen, G., Rong, X., Zhang, Y., Song, L., & Shang, X. (2024) · Sensors · doi:10.3390/s24165392 · Cited by 1

IMP‐DETR: Optimization model for defect detection of injection‐moulded products

Liu, A., & Han, L. (2024) · The Journal of Engineering · doi:10.1049/tje2.70032 · Cited by 1

Automated Inspection of Flywheel Metallic Objects Using Deep Learning-Based Defect Detection

Sanjaya, S. D., & Lin, H.-I. (2024) · International Automatic Control Conference · doi:10.1109/cacs63404.2024.10773223 · Cited by 1

Test-Time Feature Caching Network for cross-domain multilayer ceramic capacitors defect detection

Hsu, C.-Y., & Lu, Y. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113380 · Cited by 1

Optimizing Convolutional Neural Networks for Real-Time Fabric Defect Detection

Shafa, J. A., Rahman, A., Tonni, S. I., & Haque, A. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11021765 · Cited by 1

Real-Time Steel Surface Defect Detection Using YOLOv8 and OpenCV: Enhancing Accuracy and Efficiency in Industrial Applications

Zhang, Y., Ashraf, H., Kahar, S., Khan, A., Wicaksana, F. A., & Junfithrana, A. P. (2025) · International Conference on Metaverse and Current Trends in Computing (ICMCTC) · doi:10.1109/icmctc62214.2025.11196532 · Cited by 1

Performance Evaluation of Transfer Learning for Surface Defect Classification

Benbarrad, T., Arioua, M., & Hatim, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-94191-8_79 · Cited by 1

Crack Detection Learning Structure Using Cross Pseudo Supervision with Stable Diffusion ModelPreprint

Shim, S., & Kang, S.-M. S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4836212 · Cited by 1

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

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

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 258

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 78

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 174