A multi-class classifier based on support vector hyper-spheres for steel plate surface defects
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The watershed algorithm can well detect weak edges, and even though noise in an image may cause over-segmentation, it can still ensure a closed continuous edge.
“The watershed algorithm can well detect weak edges, even though the noise in an image may cause over-segmentation, it can still ensure a closed continuous edge.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [45]
Chu et al. made contributions to surface defect classification based on a multiclass SVM by improving the historic problems of imbalanced training samples and noise robustness.
“Notably, Chu et al. made great contributions to surface defect classification based on a multiclass SVM, by improving the historic problems of imbalanced training samples [120] and noise robustness [45].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [45]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
An End‐to‐End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks
Yi, L., Li, G., & Jiang, M. (2017) · steel research international · doi:10.1002/srin.201600068 · Cited by 173 · see citing works
Optimization of the recognition of defects in flat steel products with the cost matrices theory
DuPont, F., Odet, C., & Cartont, M. (1997) · NDT & E International · doi:10.1016/s0963-8695(96)00045-x · Cited by 47 · see citing works
Indefinite core vector machine
Schleif, F.-M., & Tino, P. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.06.003 · Cited by 26 · see citing works
Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects
Chu, M.-X., Wang, A.-N., Gong, R.-F., & Sha, M. (2014) · Journal of Iron and Steel Research International · doi:10.1016/s1006-706x(14)60027-3 · Cited by 21 · see citing works
Self-citationThe Strip Steel Surface Defects Classification Method Based on Weak Classifier Adaptive Enhancement
Yunhui, Y., Kechen, S., Zhitao, X., & Xuehui, F. (2011) · 2011 Third International Conference on Measuring Technology and Mechatronics Automation · doi:10.1109/icmtma.2011.811 · Cited by 11 · see citing works
Real-Time Steel Inspection System Based on Support Vector Machine and Multiple Kernel Learning
Chen, Y., Chen, L., Liu, X., Ding, S., & Zhang, H. (2011) · Advances in Intelligent and Soft Computing · doi:10.1007/978-3-642-25658-5_21 · Cited by 9 · see citing works
Gong, R., Wu, C., Chu, M., & Liu, X. (2016) · steel research international · doi:10.1002/srin.201600049 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
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
- Authors
- Gong, R., Chu, M., Yang, Y., & Feng, Y.
- Affiliations
- University of Science and Technology Liaoning (CN)
- Corresponding author
- Chu, Maoxiang
- Yang, Yonghui
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 188
- Pages
- 70-78
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W2924161931
- Cited by
- 31
- References
- 7
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 3 · 2022: 5 · 2021: 5 · 2020: 8 · 2019: 1
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Chemometrics and Intelligent Laboratory Systems (publishedVersion)
- Funders
- Liaoning Province Ministry of Education Scientific Study Project (2017LNQN11, 2016TSPY13)
- Liaoning Province PhD Start-up Fund (201601291)
- Liaoning Province Natural Fund Project (20180550067)
- 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 › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- multi-class classification, pinball loss, noise immunity, label noise, defect classification
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
