Support-vector-machine-based method for automated steel bridge rust assessment
Support-vector-machine-based method for automated steel bridge rust assessment
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DOI：
10.1016/j.autcon.2011.12.001
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发表时间：
2012-05-01
影响因子：
10.3
通讯作者：
Chang, Luh-Maan
中科院分区：
文献类型：
--
作者：
Chen, Po-Han;Shen, Heng-Kuang;Chang, Luh-Maan
多年来，土木工程领域一直采用计算机方法进行结构健康监测和缺陷识别。但仍存在光照不均匀的问题，需要进一步研究解决。针对这一问题，提出了一种基于支持向量机的钢桥锈蚀识别新方法。SVMRA将傅立叶变换和支持向量机相结合，为非均匀光照下的铁锈图像识别提供了一种有效的方法。与流行的简化K-均值算法(SKMA)和BE-ANFIS算法进行了比较，结果表明，该算法在处理非均匀光照和红色、棕色背景下的铁锈图像时具有更好的性能。(C)2011爱思唯尔B.V.保留所有权利。
Computerized methods have been used for structure health monitoring and defect recognition in the civil engineering field for many years. However, there are still non-uniform illumination problems that require more research efforts to resolve.In view of this, a new support-vector-machine-based rust assessment approach (SVMRA) is developed in this research for steel bridge rust recognition. SVMRA combines Fourier transform and support vector machine to provide an effective method for non-uniformly illuminated rust image recognition. After comparison with the popular simplified K-means algorithm (SKMA) and BE-ANFIS, it is shown that the proposed SVMRA performs more effectively in dealing with non-uniform illumination and rust images of red- and brown-color background over SKMA and BE-ANFIS. (C) 2011 Elsevier B.V. All rights reserved.