Weld Imperfection Classification by Texture Features Extraction and Local Binary Pattern-Reference-Cited by-同舟云学术

Weld Imperfection Classification by Texture Features Extraction and Local Binary Pattern

Published:2021 Issue: Volume: Page:367-378
ISSN:2190-3018
Container-title:Modeling, Simulation and Optimization
language:
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Author:

Patil Rajesh V.,Reddy Y. P.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-9829-6_28

Reference16 articles.

1. Zhang, H., Chen, Z., Zhang, C., Xi, J., Le, X.: Weld defect detection based on deep learning method. In: IEEE 15th International Conference on Automation Science and Engineering, pp. 1574–1579 (2019)

2. Li, Y., Gao, W.: Research on X-ray welding image defect detection based on convolution neural network. IOP Conf. Ser. J. Phys. Conf. Ser. 1237(032005), 1–7 (2019)

3. Shaohua, D., Xuan, S., Shuyi, X., Mingfeng, W.: Automatic defect identification technology of digital image of pipeline weld. Natural Gas Industry B 6, 399–403 (2019)

4. Hou, W., Wei, Y., Guo, J., Jin, Y., Zhu, C.: Automatic detection of welding defects using deep neural network. IOP Conf. Ser. J. Phys. Conf. Ser. 933, 6–12 (2018)

5. Comas, T.F., Diao, C., Ding, J., Williams, S., Zhao, Y.: A passive imaging system for geometry measurement for the plasma arc welding process. IEEE Trans. Industrial Electronics 64(9), 7201–7209 (2017)

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