STDPNet: a dual-path surface defect detection neural network based on shearlet transform
Author:
Funder
National Natural Science Foundation of China
Department of Education of Liaoning Province
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-03139-8.pdf
Reference35 articles.
1. Song, K., Yan, Y.: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf. Sci. 285, 858–864 (2013). https://doi.org/10.1016/j.apsusc.2013.09.002
2. Hao, R., Lu, B., Cheng, Y., Li, X., Huang, B.: A steel surface defect inspection approach towards smart industrial monitoring. J. Intell. Manuf. 32, 1833–1843 (2021). https://doi.org/10.1007/s10845-020-01670-2
3. Chen, S., Yu, J., Xu, X., Chen, Z., Lu, L., Hu, X., Yang, Y.: Split-guidance network for salient object detection. Vis. Comput. 39, 1437–1451 (2023). https://doi.org/10.1007/s00371-022-02421-5
4. Neogi, N., Mohanta, D.K., Dutta, P.K.: Review of vision-based steel surface inspection systems. EURASIP J. Image Video Process. 2014(1), 1–19 (2014). https://doi.org/10.1186/1687-5281-2014-50
5. Schwartz, W.R., da Silva, R.D., Davis, L.S., Pedrini, H.: A novel feature descriptor based on the shearlet transform. In: 2011 18th IEEE International Conference on Image Processing, pp. 1033–1036. IEEE (2011). https://doi.org/10.1109/ICIP.2011.6115600
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