Surface defects inspection of cold rolled strips based on neural network
Abstract
Defects on the surface of steel strips are main factors to evaluate quality of steel strips, surface inspection is of great importance to improve quality of steel strips. Traditional surface inspection by human inspectors is far from satisfactory. In this paper, an approach to detect surface defects of steel strips based on feed-forward neural network (FFN) is discussed. The experiments show that the method is effective.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Steel surfaces exhibit many types of defects, and NN-BP has been extensively used to classify such defects.
“Steel surfaces exhibit many types of defects, and NNBP has been extensively used to classify such defects [31,36,54,59,63,72,78,80].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [78]
Cites
Training feedforward networks with the Marquardt algorithm
Hagan, M., & Menhaj, M. B. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.329697 · Cited by 7,709 · see citing works
Defect detection in textured materials using Gabor filters
Kumar, A., & Pang, G. K. H. (2002) · IEEE Transactions on Industry Applications · doi:10.1109/28.993164 · Cited by 475 · see citing works
Fabric defect detection by Fourier analysis
Chan, C.-H., & Pang, G. K. H. (2000) · IEEE Transactions on Industry Applications · doi:10.1109/28.871274 · Cited by 443 · see citing works
Fabric defect detection by Fourier analysis
Chan, C.-H., & Pang, G. K. H. (2003) · doi:10.1109/ias.1999.805975 · Cited by 331 · 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
Automated surface inspection for statistical textures
Tsai, D., & Huang, T.-Y. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00007-6 · Cited by 217 · see citing works
Automated surface inspection for directional textures
Tsai, D., & Hsieh, C.-Y. (1999) · Image and Vision Computing · doi:10.1016/s0262-8856(99)00009-8 · Cited by 214 · see citing works
Vision system for on-loom fabric inspection
Sari‐Sarraf, H., & Goddard, J. S. (1999) · IEEE Transactions on Industry Applications · doi:10.1109/28.806035 · Cited by 167 · see citing works
Texture analysis and discrimination in additive noise
Liu, S.-S., & Jernigan, M. E. (1990) · Computer Vision Graphics and Image Processing · doi:10.1016/0734-189x(90)90162-o · Cited by 125 · see citing works
Detection of local defects in textile webs using Gabor filters
Escofet, J., Navarro, R., Garcia-Verela, M. S. M., & Pladellorens, J. M. (1998) · Optical Engineering · doi:10.1117/1.601751 · Cited by 118 · see citing works
Texture characterization and defect detection using adaptive wavelets
Jasper, W. J., Garnier, S. J., & Potlapalli, H. (1996) · Optical Engineering · doi:10.1117/1.601054 · Cited by 107 · see citing works
On-line textile quality control using optical Fourier transforms
Castellini, C., Francini, F., Longobardi, G., Tiribilli, B., & Sansoni, P. (1996) · Optics and Lasers in Engineering · doi:10.1016/0143-8166(95)00044-o · Cited by 38 · see citing works
Wavelet analysis to fabric defects detection in weaving processes
Kim, S., Lee, M. H., & Woo, K.-B. (2003) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.1999.796918 · Cited by 35 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Automated Visual Defect Detection for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2963555 · Cited by 559 · 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 · see citing works
“Steel surfaces exhibit many types of defects, and NN-BP has been extensively used to classify such defects [31,36,54,59,63,72,78,80].”
BackgroundRecent advances in surface defect inspection of industrial products using deep learning techniques
Zheng, X., Zheng, S., Kong, Y., & Chen, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06592-8 · Cited by 287 · see citing works
Xue-wu, Z., Yan-qiong, D., Yan-yun, L., Ai-ye, S., & Rui-yu, L. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.11.030 · Cited by 188 · see citing works
Wang, S., Xia, X., Ye, L., & Yang, B. (2021) · Metals · doi:10.3390/met11030388 · Cited by 184 · see citing works
Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials
Fang, X., Luo, Q., Zhou, B., Li, C., & Tian, L. (2020) · Sensors · doi:10.3390/s20185136 · Cited by 166 · 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 cost-effective and automatic surface defect inspection system for hot-rolled flat steel
Luo, Q., & He, Y. (2016) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2015.09.008 · Cited by 129 · see citing works
A novel deep convolutional neural network algorithm for surface defect detection
Zhang, D., Hao, X., Liang, L., Liu, W., & Qin, C. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac071 · Cited by 109 · 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
Visual techniques for defects detection in steel products: A comparative study
Mordia, R., & Verma, A. K. (2022) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2022.106047 · Cited by 97 · see citing works
Xia, J., Khabaz, M. K., Patra, I., Khalid, I., Álvarez, J. R. N., Rahmanian, A., Eftekhari, S. A., & Toghraie, D. S. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.06.009 · Cited by 72 · see citing works
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
Steel Surface Defect Detection Using Texture Segmentation Based on Multifractal Dimension
Yazdchi, M., Yazdi, M., & Mahyari, A. G. (2009) · 2009 International Conference on Digital Image Processing · doi:10.1109/icdip.2009.68 · Cited by 65 · see citing works
Wang, Y., Xia, H., Yuan, X., Li, L., & Sun, B. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5238-0 · Cited by 61 · see citing works
Defect inspection system for steel wire rods produced by hot rolling process
Yun, J. P., Choi, D.-C., Jeon, Y.-J., Park, C., & Kim, S. W. (2013) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-013-5397-8 · Cited by 50 · see citing works
Automated visual classification of frequent defects in flat steel coils
Medina, R., Gayubo, F., González-Rodrigo, L. M., Olmedo, D., Gómez-García-Bermejo, J., Zalama, E., & Perán, J. R. (2011) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-011-3352-0 · Cited by 49 · see citing works
A fuzzy inference system applied to defect detection in flat steel production
Borselli, A., Colla, V., Vannucci, M., & Veroli, M. (2010) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzzy.2010.5584036 · Cited by 47 · see citing works
A fast method of steel surface defect detection using decision trees applied to LBP based features
Aghdam, S. R., Amid, E., & Imani, M. F. (2012) · 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2012.6360951 · Cited by 46 · 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
Defect detection of steel surface using entropy segmentation
Nand, G. K., Noopur, & Neogi, N. (2014) · 2014 Annual IEEE India Conference (INDICON) · doi:10.1109/indicon.2014.7030439 · Cited by 44 · see citing works
Vision-based defect detection of scale-covered steel billet surfaces
Yun, J. P., Choi, S., & Kim, S. W. (2009) · Optical Engineering · doi:10.1117/1.3102066 · Cited by 44 · see citing works
A Non-Invasive Technique for Online Defect Detection on Steel Strip Surfaces
Bulnes, F. G., García, D. F., de la Calle, F. J., Usamentiaga, R., & Molleda, J. (2016) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-016-0370-8 · Cited by 42 · see citing works
Surface Inspection System of Steel Strip Based on Machine Vision
Tang, B., Kong, J., Wang, X., & Chen, L. (2009) · Database Technology and Applications · doi:10.1109/dbta.2009.133 · Cited by 39 · see citing works
Real-Time Defects Detection Algorithm For High-Speed Steel Bar In Coil
Choi, S. H., Yun, J. P., Seo, B., Park, Y., & Kim, S. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1333681 · Cited by 34 · see citing works
Ma, Z., Li, Y., Huang, M., & Deng, N. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.06.016 · Cited by 28 · see citing works
Multi-modal defect detection of residual oxide scale on a cold stainless steel strip
Martín, D., Guinea, D., García‐Alegre, M. C., Villanueva, E., & Guinea, D. (2010) · Machine Vision and Applications · doi:10.1007/s00138-010-0260-5 · Cited by 28 · see citing works
A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection
Gyimah, N. K., Girma, A., Mahmoud, M. N., Nateghi, S., Homaifar, A., & Opoku, D. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9659140 · Cited by 26 · see citing works
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 24 · see citing works
Adaptive-MAML: Few-shot metal surface defects diagnosis based on model-agnostic meta-learning
Pang, S., Zhang, L., Yuan, Y., Zhao, W., Wang, S., & Wang, S. (2023) · Measurement · doi:10.1016/j.measurement.2023.113612 · Cited by 22 · see citing works
Development of Real-time Defect Detection Algorithm for High-speed Steel Bar in Coil(BIC)
Yun, J., Park, Y., Seo, B., Kim, S., Choi, S. W., Park, C., Bae, H., & Hwang, H. W. (2006) · SICE-ICASE International Joint Conference · doi:10.1109/sice.2006.314680 · Cited by 21 · see citing works
Lightweight Network-Based Surface Defect Detection Method for Steel Plates
Wang, C., Sun, M., Cao, Y., He, K., Zhang, B., Cao, Z., & Wang, M. (2023) · Sustainability · doi:10.3390/su15043733 · Cited by 20 · see citing works
Zhang, K., Tsang, Y. P., Lee, C. K. M., & Wu, C. H. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.03.017 · Cited by 17 · see citing works
Svetlík, J., Malega, P., Rudy, V., Rusnák, J., & Kováč, J. (2021) · Materials · doi:10.3390/ma14071641 · Cited by 16 · see citing works
Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks
Lee, S., & Son, Y. (2021) · Mathematics · doi:10.3390/math9121367 · Cited by 16 · see citing works
Defects Detection of Billet Surface Using Optimized Gabor Filters
Yun, J. P., Choi, S. H., Seo, B., Park, C. H., & Kim, S. W. (2008) · IFAC Proceedings Volumes · doi:10.3182/20080706-5-kr-1001.00012 · Cited by 16 · see citing works
Automatic Detection and Quantification of Hot-Rolled Steel Surface Defects Using Deep Learning
Liu, Z., Zeng, Z., Li, J., & Teng, S. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-022-07567-x · Cited by 15 · see citing works
Swin-Transformer -YOLOv5 for lightweight hot-rolled steel strips surface defect detection algorithm
Wang, Q., Dong, H., & Huang, H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0292082 · Cited by 16 · see citing works
Surface defect classification of steel strip based on machine vision
Singhka, D. K. H., Neogi, N., & Mohanta, D. K. (2014) · doi:10.1109/iccct2.2014.7066698 · Cited by 12 · see citing works
The Copper Surface Defects Inspection System Based on Computer Vision
Wang, P., Zhang, X., Mu, Y., & Wang, Z. (2008) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2008.273 · Cited by 11 · see citing works
Inspection of metallic surfaces using Local Binary Patterns
Mansano, M. D. R., Pavesi, L., Oliveira, L. S., Britto, A., & Koerich, A. L. (2011) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2011.6119655 · Cited by 10 · see citing works
The system of copper strips surface defects inspection based on intelligent fusion
Zhang, X., Liang, R., Ding, Y., Chen, J., Duan, D., & Zong, G. (2008) · IEEE International Conference on Automation and Logistics (ICAL) · doi:10.1109/ical.2008.4636198 · Cited by 10 · see citing works
Advanced predictive quality control strategy involving different facilities
Ordieres-Meré, J. B., González‐Marcos, A., Alba‐Elías, F., & Menéndez-Fernández, C. (2012) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-012-4562-9 · Cited by 9 · see citing works
Copper Strip Surface Defects Inspection Based on SVM-RBF
Liang, R., Ding, Y., Zhang, X., & Chen, J. (2008) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2008.271 · Cited by 9 · see citing works
Zhang, H., Liu, H., Guo, R., Liang, L., Liu, Q., & Ma, W. (2024) · Sensors · doi:10.3390/s24144630 · Cited by 8 · see citing works
Self-citationDubey, P., Miller, S., Günay, E. E., Jackman, J. K., Kremer, G. E. O., & Kremer, P. A. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102727 · Cited by 8 · see citing works
The development and prospect of surface defect detection based on vision measurement method
Yuan, L., Zhang, Z., & Tao, X. (2016) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2016.7578461 · Cited by 7 · see citing works
Detecting the surface defects on hot rolled steel sheets using texture analysis
Sarma, A. S. S., Janani, R., & Sarma, A. S. V. (2013) · International Conference on Advanced Electronic Systems (ICAES) · doi:10.1109/icaes.2013.6659382 · Cited by 6 · see citing works
Surface Defect Detection and Recognition Based on CNN
Evstafev, O., & Shavetov, S. V. (2022) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit55151.2022.9803911 · Cited by 5 · see citing works
Research on Surface Quality Evaluation System of Steel Strip Based on Computer Vision
Zhao, J., Weiwei, L., Feng, Y., Jun, L., Zhang, Y., & Yunhui, Y. (2009) · Intelligent Information Technology Application · doi:10.1109/iita.2009.45 · Cited by 5 · see citing works
Development of defect detection algorithm in cold rolling
Yun, S. W., Kong, N., Lee, G., & Park, P. (2008) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.1109/iccas.2008.4694507 · Cited by 4 · see citing works
You Only Look Once v5 and Multi-Template Matching for Small-Crack Defect Detection on Metal Surfaces
Dubey, P., Miller, S., Günay, E. E., Jackman, J. K., Kremer, G. E. O., & Kremer, P. A. (2025) · Automation · doi:10.3390/automation6020016 · Cited by 2 · see citing works
Machine-vision detection for rail-steel’s surface flaws based on quantum neural network
Wang, X., Tang, Y., & Cheng, P. (2008) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2008.4593749 · Cited by 2 · see citing works
A deep transfer learning-based algorithm for concrete surface defect detection
Jin, Z., & Wang, L. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad5e34 · Cited by 1 · see citing works
Research of Neural Network Methods of Visual Defectoscopy of Metal Structures
Chernikov, A., Smirnova, E., & Zhavoronkov, O. (2023) · International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) · doi:10.1109/icieam57311.2023.10139006 · Cited by 1 · see citing works
Xu, S., & Chen, X. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/493/1/012005 · Cited by 1 · see citing works
Visual surface inspection of scale-covered steel billets
Yun, J. P., Choi, S., Seo, B., & Kim, S. W. (2008)
Research on the recognition of surface defects in copper strip based on fuzzy neural network
Zhang, X., Lv, Y.-Y., Ding, Y., & Zhen-tao, Z. (2008) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/iccis.2008.4670927
Robust defect detection method for improving inspection process
Iwasawa, T., Lu, X., Tanaka, M., & Suzuki, T. (2018) · Total Quality Science · doi:10.17929/tqs.4.13
Predicting Strip Tearing in Cold Rolling Tandem Mill using Neural Network
Haghani, A., Khoogar, A. R., & Kumarci, F. (2015) · DOAJ (DOAJ: Directory of Open Access Journals)
Novodranov, A. S. (2024) · The Paton Welding Journal · doi:10.37434/tpwj2024.08.05
- Authors
- Kang, G.-W., & Liu, H.-B.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Venue
- 2005 International Conference on Machine Learning and Cybernetics
- Publisher
- IEEE
- Published
- 2005
- Pages
- 5034-5037 Vol. 8
- OpenAlex
- W2139963745
- Cited by
- 62
- References
- 13
- Citations by year
- 2025: 2 · 2024: 6 · 2023: 5 · 2022: 5 · 2021: 5 · 2020: 3 · 2019: 1 · 2018: 2 · 2017: 1 · 2016: 3 · 2015: 2 · 2014: 3 · 2013: 2 · 2012: 2
- FWCI
- 9.91
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning and Cybernetics (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- surface defect detection, cold-rolled steel strip, feedforward neural networks, automated visual inspection, steel strip quality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
