|本期目录/Table of Contents|

[1]刘立峰,武奇生,姚博彬.基于高斯尺度空间和SVM的桥梁裂缝检测研究[J].工业仪表与自动化装置,2019,(01):13-14.[doi:1000-0682(2019)01-0003-05]
 LIU Lifeng,WU Qisheng,YAO Bobin.Bridge crack detection based on Gaussian scale space and SVM[J].Industrial Instrumentation & Automation,2019,(01):13-14.[doi:1000-0682(2019)01-0003-05]
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基于高斯尺度空间和SVM的桥梁裂缝检测研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2019年01期
页码:
13-14
栏目:
出版日期:
2019-02-15

文章信息/Info

Title:
Bridge crack detection based on Gaussian scale space and SVM
作者:
刘立峰武奇生姚博彬
长安大学 电子与控制工程学院,西安 710064
Author(s):
LIU Lifeng WU Qisheng YAO Bobin
School of Electronic and Control Engineering,Chang’an University, Xi’an 710064, China
关键词:
桥梁裂缝分类高斯尺度空间背景差支持向量机(SVM)
Keywords:
bridge crack classification gaussian scale space poor background support vector machine
分类号:
TP391
DOI:
1000-0682(2019)01-0003-05
文献标志码:
A
摘要:
针对现有的桥梁裂缝检测及分类算法在光照不均匀条件下,存在检测精度不高、分类效果不理想的问题,提出了一种基于高斯尺度空间与支持向量机(sopport vector machine, SVM)多分类器相结合的桥梁裂缝检测及分类算法。该文对待处理裂缝图像进行预处理,消除噪声干扰;通过裂缝图像与二维高斯函数进行卷积运算来创建高斯尺度空间,在高斯尺度空间下估计背景,利用背景差法消除光照干扰,进而提取出具有区分度的裂缝图像特征向量;利用SVM多分类器进行桥梁裂缝分类。实验结果表明所提出的算法具有较高的分类精度。
Abstract:
Aiming at the problem that the existing bridge crack detection and classification algorithm has low detection accuracy and unsatisfactory classification under the condition of uneven illumination, a Gaussian scale space and Sopport vector machine (SVM) is proposed. Bridge crack detection and classification algorithm combined with classifier. Firstly, the processed crack image is preprocessed to eliminate noise interference. Then, the Gaussian scale space is created by the convolution operation of the crack image and the two-dimensional Gaussian function. The background is estimated in the Gaussian scale space, and the illumination interference is eliminated by the background difference method. The crack image feature vector with discriminant degree is obtained. Finally, the SVM multi-classifier is used to classify the crack of the bridge. Experimental results show that the proposed algorithm has higher classification accuracy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-07-11
基金项目:中央高校基本科研业务费团队(310832173701)
作者简介:刘立峰(1994),男,长安大学电子与控制工程学院硕士研究生,研究方向为图像处理等。
更新日期/Last Update: 2019-01-15