A local annular contrast based real-time inspection algorithm for steel bar surface defects
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?
AVI techniques are more suitable to inspect surface defects on sheet materials than on wire rod/bar with minor diameter or even special-shaped structures.
“It is widely recognized that AVI techniques are more suitable to inspect surface defects on sheet materials than on wire rod/bar with minor diameter or even special-shaped structures [15].”
From Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020) · cites this paper as [15]
Cites
Proceedings of the 17th International Conference on Pattern Recognition ICPR 2004
(2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334435 · Cited by 345 · see citing works
An intelligent real-time vision system for surface defect detection
Jia, H., Murphey, Y. L., Shi, J., & Chang, T.-S. (2004) · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · doi:10.1109/icpr.2004.1334512 · Cited by 174 · see citing works
Fast mean filtering technique (FMFT)
Rakshit, S., Ghosh, A. K., & Shankar, B. U. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.02.008 · Cited by 83 · see citing works
Real-time vision-based defect inspection for high-speed steel products
Yun, J. P., Choi, S., Seo, B., & Kim, S. W. (2008) · Optical Engineering · doi:10.1117/1.2957958 · Cited by 59 · see citing works
Agarwal, K., Shivpuri, R., Zhu, Y., Chang, T.-S., & Huang, H. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.12.026 · Cited by 54 · 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
On-Line Seam Detection in Rolling Processes Using Snake Projection and Discrete Wavelet Transform
Li, J., Shi, J., & Chang, T. (2007) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.2752519 · Cited by 24 · see citing works
A computer vision system for automatic steel surface inspection
Liu, Y.-C., Hsu, Y.-L., Sun, Y.-N., Tsai, S.-J., Ho, C.-Y., & Chen, C.-M. (2010) · 2010 5th IEEE Conference on Industrial Electronics and Applications · doi:10.1109/iciea.2010.5515197 · Cited by 23 · 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
Detection of scratch defects for wire rod in steelmaking process
Zhang, J., Kang, D., & Won, S. (2010) · doi:10.1109/iccas.2010.5670210 · Cited by 11 · see citing works
Self-citationA New Fast Border Search Algorithm and Its Expansion
Zhang, H., Wu, G., Sun, X., Xu, J., & Xu, K. (2007) · IEEE International Conference on Automation and Logistics (ICAL) · doi:10.1109/ical.2007.4338881 · Cited by 7 · see citing works
Flagged first, then by citations
1 of the 12 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Song, K., & Yan, Y. (2013) · Applied Surface Science · doi:10.1016/j.apsusc.2013.09.002 · Cited by 1,026 · see citing works
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
“Spatial domain techniques are fast and easy to implement compared to wavelet-based methods, and they have been used in most of the papers where suitability for real-time operation have also been reported [2,24,26,35,46,58,59,61,76].”
BackgroundMethodInfluentialReview 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
An improved Otsu method using the weighted object variance for defect detection
Yuan, X., Wu, L., & Peng, Q. (2015) · Applied Surface Science · doi:10.1016/j.apsusc.2015.05.033 · Cited by 230 · 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
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
Chu, M., Gong, R., Gao, S., & Zhao, J. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.10.020 · Cited by 97 · see citing works
Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN
Sun, X., Gu, J., Huang, R., Zou, R., & Girón-Palomares, B. (2019) · Electronics · doi:10.3390/electronics8050481 · Cited by 76 · see citing works
Chu, M., Liu, X., Gong, R., & Liu, L. (2018) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2018.03.014 · Cited by 45 · see citing works
Chu, M., Zhao, J., Liu, X., & Gong, R. (2017) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.07.008 · Cited by 44 · see citing works
A novel surface defect inspection algorithm for magnetic tile
Xie, L., Lin, L., Yin, M., Meng, L., & Yin, G. (2016) · Applied Surface Science · doi:10.1016/j.apsusc.2016.03.013 · Cited by 41 · see citing works
A lower envelope Weber contrast detection algorithm for steel bar surface pit defects
Li, W.-B., Lu, C., & Zhang, J. (2012) · Optics & Laser Technology · doi:10.1016/j.optlastec.2012.05.016 · Cited by 37 · see citing works
Self-citationSingh, A. R., Bashford‐Rogers, T., Marnerides, D., Debattista, K., & Hazra, S. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10763-6 · Cited by 31 · see citing works
A Fast Detection Method for Region of Defect on Strip Steel Surface
Gong, R., Chu, M., Wang, A., & Yang, Y. (2015) · ISIJ International · doi:10.2355/isijinternational.55.207 · Cited by 18 · see citing works
Recognition Of Surface Defects On Steel Sheet Using Transfer LearningPreprint
Fu, J., Zhu, X., & Li, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03258 · Cited by 18 · see citing works
Jeon, Y.-J., Choi, D.-C., Yun, J. P., & Kim, S. W. (2015) · ISIJ International · doi:10.2355/isijinternational.isijint-2015-053 · Cited by 17 · see citing works
Wen, X., Song, K., Niu, M., Dong, Z., & Yan, Y. (2016) · Optik · doi:10.1016/j.ijleo.2016.11.050 · Cited by 15 · see citing works
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 · see citing works
Gong, Y., Luo, J. T., Shao, H., He, K., & Zeng, W. (2020) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-020-0668-4 · Cited by 16 · see citing works
Wang, C., & Wang, Y. (2024) · Sensors · doi:10.3390/s24134088 · Cited by 14 · see citing works
Dynamic Yarn-Tension Detection Using Machine Vision Combined with a Tension Observer
Ji, Y., Ma, J., Zhou, Z., Li, J., & Song, L. (2023) · Sensors · doi:10.3390/s23083800 · Cited by 13 · see citing works
Integration of digital quality control for intelligent manufacturing of industrial ceramic tiles
Coşkun, H., Yi̇ği̇t, T., & Üncü, İ. S. (2022) · Ceramics International · doi:10.1016/j.ceramint.2022.05.224 · Cited by 12 · see citing works
Zhao, L., Ouyang, Q., Chen, D., Udupa, J. K., Wang, H., & Zeng, Y. (2014) · Review of Scientific Instruments · doi:10.1063/1.4901222 · Cited by 11 · see citing works
Ma, L., Du, Y., Liu, Z., & Ma, L. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04533-0 · Cited by 11 · see citing works
Steel surface defects analysis with machine vision and deep learning
Frydrych, K., Tomczak, M., Jasinski, J. J., & Papanikolaou, S. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16539-y · Cited by 10 · see citing works
Tout, K., Cogranne, R., & Retraint, F. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.10.030 · Cited by 10 · see citing works
Virtual-sample-based defect detection algorithm for aluminum tube surface
Lang, N., Wang, D., Cheng, P., Zuo, S., & Zhang, P. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/abf865 · Cited by 10 · see citing works
AI Landing for Sheet Metal-Based Drawer Box Defect Detection Using Deep Learning (ALDB-DL)
Sheu, R., Chen, L.-C., Pardeshi, M. S., Pai, K.-C., & Chen, C.-Y. (2021) · Processes · doi:10.3390/pr9050768 · Cited by 9 · see citing works
Automated vision based detection of blistering on metal surface: For robot
Tan, W. C., Goh, P. C., Causo, A., Chen, I., & Tan, H. K. (2017) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2017.8256078 · Cited by 8 · see citing works
Steel Defect Classification Using Machine Learning
Arshad, S. R., Obaid, I., Gull, R., & Shahzad, M. (2022) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom53663.2022.9721728 · Cited by 8 · see citing works
A Method of Steel Bar Image Segmentation Based on Multi-Attention U-Net
Shi, J., Wu, K., Yang, C., & Deng, N. (2021) · IEEE Access · doi:10.1109/access.2021.3052224 · Cited by 8 · see citing works
Zhang, Z., Liu, W., Ostrosi, E., Tian, Y., & Yi, J. (2020) · Engineering Computations · doi:10.1108/ec-11-2019-0502 · Cited by 7 · see citing works
Effect of Contact Point of Wire Ring on Cooling Behavior during Stelmor Cooling
Hwang, J.-K. (2022) · Materials · doi:10.3390/ma15228262 · Cited by 7 · see citing works
Singh, S. A., & Desai, K. A. (2025) · Measurement · doi:10.1016/j.measurement.2025.118134 · Cited by 4 · see citing works
Defects extraction for QFN based on mathematical morphology and modified region growing
Chen, K., Zhang, Z., Yuan, C., Dai, M., & Shi, J. (2015) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2015.7237867 · Cited by 4 · see citing works
Rail surface defect inspection via a self-reference template and similarity evaluation
Lang, N., Wang, D., Cheng, P., & Liu, L. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac31ca · Cited by 3 · see citing works
Design of Automatic Detection Device for Steel Bar Surface Defects
Zhang, J. C., Li, W. B., & Lu, C. H. (2012) · Advanced materials research · doi:10.4028/www.scientific.net/amr.532-533.390 · Cited by 2 · see citing works
Self-citationAutomatic printing plate defect detection based on a simplified homocentric square filter
Zhang, W., Xu, F., Li, X., Hu, X. G., Peng, S., & Nam, H.-D. (2016) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2016.7852732 · Cited by 2 · see citing works
Automatic Optical Inspection for steel golf club
Wu, H.-H. P., & Guo, H. (2015) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2015.7382317 · Cited by 2 · see citing works
A Distributed Heterogeneous Inspection System for High Performance In-line Surface Defect Detection
Chou, Y., Liao, W.-C., Chen, Y., Chang, M., & Lin, P. T. (2018) · Intelligent Automation & Soft Computing · doi:10.31209/2018.100000011 · Cited by 2 · see citing works
A Composite 3D-2D Visual Surface Defects Inspection Method for Steel Bar Intelligent Manufacturing
Xu, F., Wang, J., Liu, G., & Wang, K. (2020) · International Conference on Data Science and Business Analytics (ICDSBA) · doi:10.1109/icdsba51020.2020.00080 · Cited by 1 · see citing works
A Review Paper on Product Surface Defect Detection of Ironing Process
Faizin, A., Wahjudi, A., Batan, I. M. L., & Pramono, A. S. (2016) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.836.147 · Cited by 1 · see citing works
Non-concentric Circular Texture Removal for Workpiece Defect Detection
Qin, S., Guo, D., Chen, H., & Xi, N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27538-9_49 · Cited by 1 · see citing works
Jeong, K.-H., Lee, D., Lee, S., Lim, S.-J., & Moon, H.-K. (2023) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-023-00941-1 · Cited by 1 · see citing works
Analysis of Steel Bar Surface Defects Based on Machine Vision
Li, W. B., Lu, C. H., & Zhang, J. C. (2012) · Advanced materials research · doi:10.4028/www.scientific.net/amr.549.1017 · Cited by 1 · see citing works
Self-citationReview of Vision Inspection Technology for Surface Defect of Steel Bar
Li, W. B., Zhang, Q. Z., Sun, J., Liu, L., & He, S. L. (2015) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.740.543 · Cited by 1 · see citing works
Self-citationHistogram-Based Image Enhancement and Analysis for Steel Surface and Defects Images
Kumar, R., Soni, A. K., Soni, A., & Gupta, S. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-5078-9_51 · Cited by 1 · see citing works
An Intelligent Image Processing System for Real-Time Detection of Surface Flaws
Yu-Cheng, C., Liao, W.-C., Chang, M., & Lin, P. T. (2016) · International Journal of Automation and Smart Technology · doi:10.5875/ausmt.v6i1.1006
Surface defect detection for anodized aluminum tube based on automatic optical inspection
Wu, H.-H. P., Sun, H.-M., & Zhao, R. (2017) · Proceedings of International Conference on Artificial Life and Robotics · doi:10.5954/icarob.2017.os3-6
Defect Detection of Mechanical Design Products Using Deep Neural NetworkPreprint
Jan, M. A. (2022) · Research Square · doi:10.21203/rs.3.rs-1211827/v1
Particle Swarm Optimization Based Optimization for Industry Inspection
Wu, H. (2022) · Studies in systems, decision and control · doi:10.1007/978-3-031-07516-2_1
Cogranne, R. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10648199
- Authors
- Li, W.-B., Lu, C.-H., & Zhang, J.-C.
- Affiliations
- Shandong University (CN)
- Venue
- Applied Surface Science
- Publisher
- Elsevier BV
- Published
- 2012-06
- Volume
- 258
- Issue
- 16
- Pages
- 6080-6086
- ISSN
- 0169-4332 (ISSN-L)
- OpenAlex
- W2063083376
- Cited by
- 54
- References
- 12
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 3 · 2022: 6 · 2021: 6 · 2020: 5 · 2019: 4 · 2018: 4 · 2017: 5 · 2016: 5 · 2015: 6 · 2014: 2 · 2013: 1 · 2012: 3
- FWCI
- 9.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Surface Science (publishedVersion)
- Funders
- Independent Innovation Foundation of Shandong University
- 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
- defect detection, real-time inspection, image segmentation, average filtering, noise smoothing
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