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。
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.In this paper,the processed crack image is preprocessed to eliminate noise interference.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.The SVM multi-classifier is used to classify the crack of the bridge.Experimental results show that the proposed algorithm has higher classification accuracy.
作者
刘立峰
武奇生
姚博彬
LIU Lifeng;WU Qisheng;YAO Bobin(School of Electronic and Control Engineering,Chang an University,Xi an 710064,China)
出处
《工业仪表与自动化装置》
2019年第1期13-16,114,共5页
Industrial Instrumentation & Automation
基金
中央高校基本科研业务费团队(310832173701)
关键词
桥梁裂缝分类
高斯尺度空间
背景差
支持向量机(SVM)
bridge crack classification
Gaussian scale space
poor background
support vector machine(SVM)
