Surface defect classification in large-scale strip steel image collection via hybrid chromosome genetic algorithm
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 adaptive genetic algorithm and the hybrid chromosome genetic algorithm were proposed to enhance visual feature selection.
“To enhance the visual feature selection, the adaptive genetic algorithm (AGA) [91] and the hybrid chromosome genetic algorithm (HCGA) [67] were proposed.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [67]
SVM is a generalized linear classifier for binary classification and has been widely used for defect classification of flat steel surfaces.
“SVM is a generalized linear classifier for binary classification, which has been widely used for the defect classification of flat steel surfaces [67, 100-102, 118].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [67]
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
Steel defect classification with Max-Pooling Convolutional Neural Networks
Masci, J., Meier, U., Ciresan, D., Schmidhuber, J., & Fricout, G. (2012) · The 2012 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252468 · Cited by 345 · see citing works
Classification of defects in steel strip surface based on multiclass support vector machine
Hu, H., Li, Y., Liu, M., & Liang, W. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-012-1248-0 · Cited by 68 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Real-time detection of steel strip surface defects based on improved yolo detection network
Li, J., Su, Z., Geng, J., & Yin, Y. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.09.412 · Cited by 371 · see citing works
Recent 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
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
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
Wan, X., Zhang, X., & Liu, L. (2021) · Applied Sciences · doi:10.3390/app11062606 · Cited by 90 · 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
Gong, R., Wu, C., & Chu, M. (2018) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2017.11.018 · Cited by 44 · see citing works
Steel Surface Defect Classification Based on Discriminant Manifold Regularized Local Descriptor
Zhao, J., Peng, Y., & Yan, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2881962 · Cited by 26 · see citing works
- Authors
- Hu, H., Liu, Y., Liu, M., & Nie, L.
- Affiliations
- Wuhan University (CN)
- Wuhan University of Science and Technology (CN)
- National University of Singapore (SG)
- Corresponding author
- Liu, Maofu
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 181
- Pages
- 86-95
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2196029350
- Cited by
- 93
- References
- 2
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 11 · 2023: 10 · 2022: 15 · 2021: 7 · 2020: 13 · 2019: 7 · 2018: 10 · 2017: 4 · 2016: 3
- FWCI
- 9.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- National Natural Science Foundation of China (61100133, znss2013B014)
- Hubei Province Key Laboratory Open Foundation
- 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 › Evolutionary Algorithms and Applications
- Keywords
- defect classification, support vector machine, kernel function optimization, visual feature extraction, texture features, real-time classification, large-scale image databases
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
