Surface Defect Detection Method Using Saliency Linear Scanning Morphology for Silicon Steel Strip under Oil Pollution Interference
Open accessAbstract
Surface defect detection of silicon steel strip is an important section for non-destructive testing system in iron and steel industry. To detect the interesting defect objects for silicon steel strip under oil pollution interference, a new detection method based on saliency linear scanning morphology is proposed. In the proposed method, visual saliency extraction is employed to suppress the clutter background. Meanwhile, a saliency map is obtained for the purpose of highlighting the potential objects. Then, the linear scanning operation is proposed to obtain the region of oil pollution. Finally, the morphology edge processing is proposed to remove the edge of oil pollution interference and the edge of reflective pseudo-defect. Experimental results demonstrate that the proposed method presents the good performance for detecting surface defects including wipe-crack-defect, scratch-defect and small-defect.
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.
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
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,748 · see citing works
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004 · see citing works
Hands-on Morphological Image Processing
Dougherty, E. R., & Lotufo, R. (2003) · SPIE eBooks · doi:10.1117/3.501104 · Cited by 655 · see citing works
Automatic Defect Detection on Hot-Rolled Flat Steel Products
Ghorai, S., Mukherjee, A., Gangadaran, M., & Dutta, P. K. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2218677 · Cited by 317 · see citing works
Morphology-Based Crack Detection for Steel Slabs
Landstrom, A., & Thurley, M. J. (2012) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2012.2212416 · Cited by 137 · see citing works
Detection of surface defects on raw steel blocks using Bayesian network classifiers
Pernkopf, F. (2004) · Pattern Analysis and Applications · doi:10.1007/s10044-004-0232-3 · Cited by 113 · see citing works
Flaw detection in radiographic weldment images using morphological watershed segmentation technique
Alaknanda, Anand, R. S., & Kumar, P. (2008) · NDT & E International · doi:10.1016/j.ndteint.2008.06.005 · Cited by 94 · see citing works
Detection of surface crack defects on ferrite magnetic tile
Li, X., Jiang, H., & Yin, G. (2013) · NDT & E International · doi:10.1016/j.ndteint.2013.10.006 · Cited by 89 · 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
Shao, J., Du, D., Chang, B., et al. (2011) · NDT & E International · doi:10.1016/j.ndteint.2011.10.008 · Cited by 61 · 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
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
Application of multi-scale feature extraction to surface defect classification of hot-rolled steels
Xu, K., Ai, Y.-H., & Wu, X.-Y. (2013) · International Journal of Minerals, Metallurgy, and Materials · doi:10.1007/s12613-013-0690-y · Cited by 40 · see citing works
Vision-Based Sensor for Early Detection of Periodical Defects in Web Materials
Bulnes, F. G., Usamentiaga, R., García, D. F., & Molleda, J. (2012) · Sensors · doi:10.3390/s120810788 · Cited by 28 · see citing works
Pan, E., Ye, L., Shi, J., & Chang, T. (2009) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4000560 · Cited by 21 · see citing works
Flagged first, then by citations
Research works printed but not linked: 16
- [1]1. Alaknanda, R. S. Anand and P. Kumar: NDT&E Int., 42 (2009), 2.
- [2]2. J. Shao, D. Du, B. Chang and H. Shi: NDT&E Int., 46 (2012), 14.
- [3]3. X. Li, H. Jiang and G. Yin: NDT&E Int., 62 (2014), 6.
- [4]4. J. P. Yun, S. H. Choi, J. W. Kim and S. W. Kim: NDT&E Int., 42 (2009), 389.
- [5]5. E. Pan, L. Ye, J. J. Shi and T. S. Chang: J. Manuf. Sci. E-T ASME, 131 (2009), 061008.
- [6]6. A. Landström and M. Thurley: IEEE J. STSP, 6 (2012), 866.
- [7]7. F. G. Bulnes, R. Usamentiaga, D. F. García and J. Molleda: Sensors, 12 (2012), 10788.
- [8]8. R. Medina, F. Gayubo, L. M. González-Rodrigo, D. Olmedo, J. Gómez-García-Bermejo, E. Zalama and J. R. Perán: Int. J. Adv. Manuf. Tech., 57 (2011), 1087.
- [9]9. S. Ghorai, A. Mukherjee, M. Gangadaran and P. K. Dutta: IEEE T Instrum. Meas., 62(2013), 612.
- [10]10. K. Xu, Y. Ai and X. Wu: Int. J. Min. Met. Mater., 20 (2013), 37.
- [11]11. F. Pernkopf: Pattern Anal. Appl., 7 (2004), 333.
- [12]12. K. Agarwal, R. Shivpuri, Y. Zhu, T. S. Chang and H. Huang: Expert. Syst. Appl., 38 (2011), 7251.
- [13]13. J. Canny: IEEE T Pattern Anal., 6 (1986), 679.
- [14]14. R. Achanta, S. Hemami, F. Estrada and S. Susstrunk: Proc. of 27th IEEE Conf. on Computer Vision and Pattern Recognition, IEEE, New York, (2009), 1597.
- [15]15. R. Achanta and S. Susstrunk: Proc. of 17th IEEE Int. Conf. on Image Processing, IEEE, New York, (2010), 2653.
- [17]17. N. Otsu: IEEE Trans. Syst., Man, Cybern., Syst., 9 (1979), 62. Fig.
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
Surface Defect Detection Methods for Industrial Products: A Review
Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021) · Applied Sciences · doi:10.3390/app11167657 · Cited by 457 · 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
Self-citationAn 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
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
Surface Defect Saliency of Magnetic Tile
Huang, Y., Qiu, C., Guo, Y., Wang, X., & Yuan, K. (2018) · 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2018.8560423 · Cited by 117 · 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
A simulation-based few samples learning method for surface defect segmentation
Wei, T., Cao, D., Zheng, C., & Yang, Q. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.090 · Cited by 21 · 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
- Authors
- Song, K.-C., Hu, S.-P., Yan, Y.-H., & Li, J.
- Affiliations
- Northeastern University (US)
- ORCID
- Li, Jun
- Venue
- ISIJ International
- Publisher
- Iron and Steel Institute of Japan
- Published
- 2014
- Volume
- 54
- Issue
- 11
- Pages
- 2598-2607
- ISSN
- 0915-1559 (ISSN-L)
- 1347-5460
- OpenAlex
- W2018143002
- Cited by
- 41
- References
- 15
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 7 · 2019: 2 · 2018: 4 · 2017: 1 · 2016: 3 · 2015: 2
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- ISIJ International (publishedVersion)
- Funders
- National Natural Science Foundation of China (N120603003, 51374063)
- Fundamental Research Funds for the Central Universities (51374063, N120603003)
- 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, saliency maps, scratch defects, small defect detection, non-destructive testing
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