Research of Recognition Method for Surface Defects of Hot-Rolled Round Steel Based on Image Processing

Article Preview

Abstract:

The surface defects is an important factor affecting the quality of hot-rolled round steel, so the recognition of surface defects plays a very important role in the daily usage of the hot-rolled round steel. This paper aims to bring forward an appropriate method to find out the surface defects of the hot-rolled round steel under the help of Matlab software. First of all, the image edge of the round steel was detected, and the image was segmented. Secondly, the segmented image may appear bended, so it would be straightened to make the surface defects recognition easy. The third step is to eliminate image noise. Finally the processed image was analyzed and the appropriate recognition method was figured out. The results show that the method proposed in this paper is effective and accurate.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

84-89

Citation:

Online since:

February 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Zhao MC, Shan Y Y, Xiao F R, et al. Acicular ferrite formation during hot plate rolling for pipeline steels[J]. Materials Science and Technology, 2003, (19): 355-362.

DOI: 10.1179/026708303225010641

Google Scholar

[2] AcharyaT, Ray A K. Image processing: principles and applications[M]. Hoboken: John Wiley&Sons, Inc, 2005: 79-104.

Google Scholar

[3] Andreu F, Ballester C, Caselles V, et al. Minimizing total variation flow[J]. Differential and Integral Equations , 2001, 14(3): 321-360.

DOI: 10.57262/die/1356123331

Google Scholar

[4] Xu Gongwen, Zhang Zhijun, Yuan Weihua, Xu Li'na. On Medical Image Segmentation Based on Wavelet Transform[C]. ISDEA 2014. 15-16 June 2014. 671-674.

DOI: 10.1109/isdea.2014.155

Google Scholar

[5] Argenti F, Torricelli G, Alparone L. Signal-dependent noise removal in the undecimated wavelet domain[J]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002, 4 (13/14/15/16/17): 3293-3296.

DOI: 10.1109/icassp.2002.1004615

Google Scholar

[6] Chang S G, Yu B, Martin V. Adaptive wavelet thresholding for image denoising and compression[J]. IEEE Transactions on Image Processing , 2000, 9(9): 1532-1546.

DOI: 10.1109/83.862633

Google Scholar

[7] Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory , 1995, 41(3): 613- 627.

DOI: 10.1109/18.382009

Google Scholar