Abstract
A new method which is based on Support Vector Machines (SVMs) for the identification of pipeline weld defects is proposed. In order to enhance the quality of the image, a lot of actions, such as image enhancement, morphological processing and edge detection, have been dealt with. As a result, many problems, such as excessive noise, fuzzy edge and low contrast, have been solved and it’s beneficial to extract the features of the image. Firstly, the results of the identification of the second category are given. Then combined with the characteristics of multiple classifications, three structures of clustering are presented and the structure of one against one has been adopted to identify the samples after analysis. The experimental results show that the proposed model has a lot of advantages, such as the identification accuracy, high speed, easy to implement, etc, and it’s suitable for identification of pipeline weld defects.
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References
Dahai, R.: Automatic Analysis System of X-ray Real Time Imaging for Weld Line. Jointing Transaction 21(1), 61–63 (2000)
Zhang, X.: Exaction and Automatic Identification of jointing defect of radial detection. National Defense Publishing Company, Beijing (2004)
Chen, H.: Robot Used on Pipeline Detect by Ultrasonic. Robot Technique and Application (5), 28 (1995)
Chong, W.: Magnetism Dipole and Powder Detect. No Damage Check 12(3), 66–70 (1990)
Cao, X.: Whirlpool Detect of Welding Steel Tube on Product Line. South Steel (6), 33–35 (1997)
Gao, H., Sun, N.: Application of Metal Measure Method in Power Plant. Power Plant System Engineering 26(6), 55–58 (2010)
Zhang, P., Chen, J.: Spot Welding Inspection of Image Process Based on Welding Spot. Jointing Transaction 27(12), 57–60 (2006)
Zhao, X., Zhang, Y.: Performance Forecast of Spot Welding Based on Nerve Network Optimization. Jointing Transaction 27(12), 77–80 (2006)
Zhang, Z., Li, D., Zhao, H.: Function Selection of Nerve Network Detect Model. Jointing Transaction 23(3), 59–62 (2002)
Vapnik, V.: An overview of statistical learning theory. IEEE Transaction Neural Networks 10(5), 988–999 (1999)
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© 2011 Springer-Verlag Berlin Heidelberg
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Meng, W.X., Xin, G.W., Nan, T. (2011). Application Study on Detection of Pipeline Weld Defects Based on SVMs. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23777-5_37
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DOI: https://doi.org/10.1007/978-3-642-23777-5_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23776-8
Online ISBN: 978-3-642-23777-5
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