Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels

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

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · 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

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

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works

Fast R-CNNPreprint

Girshick, R. B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.08083 · Cited by 29,416 · see citing works

YOLO9000: Better, Faster, Stronger

Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,474 · see citing works

Proceedings of IEEE International Conference on Computer Vision

(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

Large-Scale Video Classification with Convolutional Neural Networks

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354 · see citing works

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works

Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition

Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction · doi:10.1109/acii.2013.90 · Cited by 366 · see citing works

Handbook of natural computing

(2013) · Choice Reviews Online · doi:10.5860/choice.50-2702 · Cited by 305 · see citing works

Self-organizing Maps

Hulle, M. M. V. (2012) · Springer eBooks · doi:10.1007/978-3-540-92910-9_19 · Cited by 114 · see citing works

Automatic detection of cracks in raw steel block using Gabor filter optimized by univariate dynamic encoding algorithm for searches (uDEAS)

Yun, J. P., Choi, S., Kim, J.-W., & Kim, S. W. (2009) · NDT & E International · doi:10.1016/j.ndteint.2009.01.007 · Cited by 67 · see citing works

Application of Shearlet transform to classification of surface defects for metals

Xu, K., Liu, S., & Ai, Y. (2015) · Image and Vision Computing · doi:10.1016/j.imavis.2015.01.001 · Cited by 55 · see citing works

Surface Detection of Continuous Casting Slabs Based on Curvelet Transform and Kernel Locality Preserving Projections

Ai, Y.-H., & Xu, K. (2013) · Journal of Iron and Steel Research International · doi:10.1016/s1006-706x(13)60102-8 · Cited by 53 · 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

Steel Surface Defects Detection and Classification Using SIFT and Voting Strategy

Suvdaa, B., Ahn, J., & Ko, J. (2013) · Cited by 44 · see citing works

Feature extraction based on contourlet transform and its application to surface inspection of metals

Ai, Y., & Xu, K. (2012) · Optical Engineering · doi:10.1117/1.oe.51.11.113605 · Cited by 26 · see citing works

Structural steel plate damage detection using DFT spectral energy and artificial neural network

Paulraj, M. P., Shukry, A. M. M., Yaacob, S., Adom, A. H., & Krishnan, P. (2010) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa.2010.5545247 · Cited by 20 · see citing works

On-Line Surface Defect Inspection System for Cold Rolled Strips

Chen, Y. (2002) · Journal of University of Science and Technology Beijing · Cited by 4 · see citing works

Review of surface defect detection of steel products based on machine vision

Tang, B., Chen, L., Sun, W. M., & Lin, Z. (2022) · IET Image Processing · doi:10.1049/ipr2.12647 · Cited by 298 · 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

Automated Visual Defect Classification for Flat Steel Surface: A Survey

Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148 · see citing works

A new graph-based semi-supervised method for surface defect classification

Wang, Y., Gao, L., Gao, Y., & Li, X. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102083 · Cited by 68 · see citing works

Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis

Kosaraju, S., Hao, J., Koh, H. M., & Kang, M. (2020) · Methods · doi:10.1016/j.ymeth.2020.05.012 · Cited by 66 · see citing works

Sonar Image Target Detection Based on Adaptive Global Feature Enhancement Network

Wang, Z., Zhang, S., Huang, W., Guo, J., & Zeng, L. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3131645 · Cited by 66 · 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

Ensemble Multiple Distinct ResNet Networks With Channel-Attention Mechanism for Multisensor Fault Diagnosis of Hydraulic Systems

Peng, X., Xu, H., Wang, J., Liu, J., & He, C. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3263924 · Cited by 44 · see citing works

Automatic recognition of surface defects for hot-rolled steel strip based on deep attention residual convolutional neural network

Huang, Z. R., Wu, J., & Xie, F. (2021) · Materials Letters · doi:10.1016/j.matlet.2021.129707 · Cited by 37 · see citing works

A Graph Guided Convolutional Neural Network for Surface Defect Recognition

Wang, Y., Gao, L., Gao, Y., & Li, X. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3140784 · Cited by 36 · see citing works

Root cause diagnosis in multivariate time series based on modified temporal convolution and multi-head self-attention

Zhou, Y., Xu, K., & He, F. (2022) · Journal of Process Control · doi:10.1016/j.jprocont.2022.06.014 · Cited by 34 · 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

Multiscale Convolutional and Recurrent Neural Network for Quality Prediction of Continuous Casting Slabs

Wu, X., Jin, H., Ye, X., Wang, J., Lei, Z., Liu, Y., Wang, J., & Guo, Y. (2020) · Processes · doi:10.3390/pr9010033 · Cited by 26 · see citing works

Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Sitek, W., & Trzaska, J. (2021) · Metals · doi:10.3390/met11111832 · Cited by 24 · see citing works

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Liu, T., Bao, J., Wang, J., & Wang, J. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1901319 · Cited by 22 · see citing works

Hot‐Rolled Steel Strip Surface Inspection Based on Transfer Learning Model

Wu, H., & Lv, Q. (2021) · Journal of Sensors · doi:10.1155/2021/6637252 · Cited by 21 · see citing works

Multi-surface defect detection for universal joint bearings via multimodal feature and deep transfer learning

Zhao, Z., Hu, B., Feng, Y., Zhao, B., Yang, C., Hong, Z., & Tan, J. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2138613 · Cited by 21 · see citing works

Multi-Directional Scene Text Detection Based on Improved YOLOv3

Xiao, L., Zhou, P., Xu, K., & Zhao, X. (2021) · Sensors · doi:10.3390/s21144870 · Cited by 18 · see citing works

Joint weakly and fully supervised learning for surface defect segmentation from images

Hu, B., Wang, X., & Yu, W. (2022) · Signal Processing Image Communication · doi:10.1016/j.image.2022.116807 · Cited by 16 · see citing works

Small samples data augmentation and improved MobileNet for surface defects classification of hot-rolled steel strips

Lin, L., Wang, Y., Zhao, S., Liu, J., Zhang, S., & Zhang, G. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.6.063056 · Cited by 14 · see citing works

Knowledge Graph-guided Convolutional Neural Network for Surface Defect Recognition

Wang, Y., Gao, L., Gao, Y., Li, X., & Gao, L. (2020) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case48305.2020.9216752 · Cited by 14 · see citing works

A New Graph-Based Method for Class Imbalance in Surface Defect Recognition

Wang, Y., Gao, L., Li, X., Gao, Y., & Xie, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3063755 · Cited by 11 · see citing works

An Improved Character Recognition Framework for Containers Based on DETR Algorithm

Zhao, X., Zhou, P., Xu, K., & Xiao, L. (2021) · Sensors · doi:10.3390/s21134612 · Cited by 11 · see citing works

Surface Defects Classification of Hot Rolled Strip Based on Few-shot Learning

Wang, W., Wu, Z., Lu, K., Long, H., Li, D., Zhang, J., Chen, P., & Wang, B. (2022) · ISIJ International · doi:10.2355/isijinternational.isijint-2021-051 · Cited by 11 · see citing works

MoL-YOLOv7: Streamlining Industrial Defect Detection With an Optimized YOLOv7 Approach

Raj, G. D., & Prabadevi, B. (2024) · IEEE Access · doi:10.1109/access.2024.3447035 · Cited by 10 · see citing works

Modeling & Evaluating the Performance of Convolutional Neural Networks for Classifying Steel Surface Defects

Chaudhry, N. J., Khan, M. B., Iqbal, M. J., & Yasir, S. M. (2022) · Journal on artificial intelligence · doi:10.32604/jai.2022.038875 · Cited by 10 · see citing works

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

Tang, W.-P., Liong, S., Chen, C., Tsai, M., Hsieh, P.-C., Tsai, Y., Chen, S.-H., & Wang, K.-C. (2021) · Applied Sciences · doi:10.3390/app11209473 · Cited by 9 · see citing works

Dual-Metric Neural Network With Attention Guidance for Surface Defect Few-Shot Detection in Smart Manufacturing

Gao, P., Wang, J., Xia, M., Qin, Z., & Zhang, J. (2023) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4063356 · Cited by 7 · see citing works

Semiautomatic Mask Generating for Electronics Component Inspection

Wu, H., Gao, W., Xu, X., & Xu, S. (2020) · IEEE Transactions on Components Packaging and Manufacturing Technology · doi:10.1109/tcpmt.2020.3033837 · Cited by 7 · see citing works

Surface Quality Evaluation of Heavy and Medium Plate Using an Analytic Hierarchy Process Based on Defects Online Detection

Zhou, D., Zhou, Y., Zhang, X., & Xu, K. (2022) · ISIJ International · doi:10.2355/isijinternational.isijint-2021-585 · Cited by 4 · see citing works

A Neural-Network-Based Watermarking Method Approximating JPEG Quantization

Yamauchi, S., & Kawamura, M. (2024) · Journal of Imaging · doi:10.3390/jimaging10060138 · Cited by 4 · see citing works

F2MCANet: joint frequency fusion and multi-channels attention neural network for surface defect recognition

Wang, T., Chen, X. D., Li, Z., Xu, Y., Zhai, Y., Zhou, Z., Yang, Y., & Tian, J. (2025) · Soft Computing · doi:10.1007/s00500-025-10931-2 · Cited by 4 · see citing works

A Hierarchical Multi-Scale Feature Fusion-Based Classification Model for Metal Defects

Li, J., & Liu, J. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10223-9 · Cited by 2 · see citing works

Pre-Trained CNN for Classification of Time Series Images of Anti-Necking Control in a Hot Strip Mill

Latham, S., & Giannetti, C. (2021) · IIAE International Conference on Industrial Application Engineering (ICIAE) · doi:10.12792/iciae2021.015 · Cited by 2 · see citing works

Inception meets Swin Transformer: A Novel Approach for Metal Defect Recognition

Tang, D., & Zhao, Y. (2024) · Academic Journal of Science and Technology · doi:10.54097/5q6vkj91 · Cited by 1 · see citing works

Detection Algorithm of Porosity and Crack Defects on Surface of Micro-precision Glass Insulated Terminals

Liu, Q., Wang, G., Wang, F., Wang, M., & HANAJIMA, N. (2020) · Proceedings of International Conference on Artificial Life and Robotics · doi:10.5954/icarob.2020.os2-2

Automation Inspection in Metal Fabrication: Enhancing Sheet Metal Forming Processes with Automation, Machine Vision and Image Correlation for Quality Assurance

Kumar, S. P., & Balasubramanian, N. V. (2024) · Journal of Advanced Manufacturing Systems · doi:10.1142/s0219686724500392

Bayesian active learning with monte carlo dropout and pseudo-labelling for steel defect classification

Chazhoor, A. A. P., Hu, S.-F., Gao, B., Ho, E. S. L., & Woo, W. L. (2026) · Multimedia tools and applications · doi:10.1007/s11042-026-21825-2

Classification of surface defects on steel sheet using convolutional neural networks

Zhou, S., Chen, Y., Zhang, D., Xie, J., & Zhou, Y. (2017) · Materiali in tehnologije · doi:10.17222/mit.2015.335 · Cited by 130 · see citing works

Surface Defect Detection in Hot-Rolled Steel Sheets Using Convolutional Neural Networks

Strgar, O., Selak, L., Požrl, T., & Podržaj, P. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11468032

Using Computer Vision to Recognize Defects on the Surface of Hot-rolled Steel

Vozmilov, A. G., Губаевич, У. В., & Анатольевич, Л. А. (2022) · International Ural Conference on Electrical Power Engineering (UralCon) · doi:10.1109/uralcon54942.2022.9906737 · Cited by 5 · see citing works

Defect detection of hot rolled steels with a new object detection framework called classification priority network

He, D., Xu, K., & Zhou, P. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.12.043 · Cited by 140 · see citing works

Automatic surface defect recognition for hot-rolled steel strip using AlexNet convolutional neural network

Boudiaf, A., Harrar, K., Benlahmidi, S., Zaghdoudi, R., Ziani, S., & Taleb, S. (2022) · International Conference on Image and Signal Processing and their Applications (ISPA) · doi:10.1109/ispa54004.2022.9786361 · Cited by 17 · see citing works