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Proceedings Paper

Classification of surface defects on bridge cable based on PSO-SVM
Author(s): Xinke Li; Chao Gao; Yongcai Guo; Yanhua Shao; Fuliang He
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Paper Abstract

Distributed machine vision system was applied for the detection on the cable surface defect of the cable-stayed bridge, and access to surface defects including longitudinal cracking, transverse cracking, surface erosion and scarring pit holes and other scars. In order to achieve the automatic classification of surface defects, firstly, part of the texture features, gray features and shape features on the defect image were selected as the target classification feature quantities; then the particle swarm optimization (PSO) was introduced to optimize the punitive coefficient and kernel function parameter of the support vector machine (SVM) model; and finally the objective of defects was identified with the help of the PSOSVM classifier. Recognition experiments were performed on cable surface defects, presenting a recognition rate of 96.25 percent. The results showed that PSO-SVM has high recognition rate for classification of surface defects on bridge cable.

Paper Details

Date Published: 21 August 2014
PDF: 6 pages
Proc. SPIE 9233, International Symposium on Photonics and Optoelectronics 2014, 92330E (21 August 2014); doi: 10.1117/12.2068638
Show Author Affiliations
Xinke Li, Chongqing Univ. (China)
Chao Gao, Chongqing Univ. (China)
Yongcai Guo, Chongqing Univ. (China)
Yanhua Shao, Chongqing Univ. (China)
Fuliang He, Chongqing Univ. (China)

Published in SPIE Proceedings Vol. 9233:
International Symposium on Photonics and Optoelectronics 2014
Zhiping Zhou, Editor(s)

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