Research on Classification of Rail Defects Based on Image Processing Algorithm

Mengying HUANG; Jiangping LUO; Wenxing WANG; Jingwei CAO

doi:10.13890/j.issn.1000-128x.2020.04.100

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Research on Classification of Rail Defects Based on Image Processing Algorithm

Test & Detection Technology | 更新时间：2021-12-10

- Research on Classification of Rail Defects Based on Image Processing Algorithm
- Electric Drive for Locomotives Issue 4, Pages: 41-46,53(2020)
- 作者机构：
  
  1.株洲时代电子技术有限公司，湖南　株洲　412007
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- DOI：10.13890/j.issn.1000-128x.2020.04.100
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Mengying HUANG, Jiangping LUO, Wenxing WANG, et al. Research on Classification of Rail Defects Based on Image Processing Algorithm. [J]. Electric Drive for Locomotives (4):41-46,53(2020)
DOI：

Mengying HUANG, Jiangping LUO, Wenxing WANG, et al. Research on Classification of Rail Defects Based on Image Processing Algorithm. [J]. Electric Drive for Locomotives (4):41-46,53(2020) DOI： 10.13890/j.issn.1000-128x.2020.04.100.

摘要

钢轨伤损的种类众多且形态各异，即便对于同类伤损，在超声波钢轨探伤检测软件中形成的B显图像也会存在差异，而当某类伤损的B显图像变化超出一定范围后，检测软件便无法识别该伤损的类别。因此，提出一种基于图像处理的钢轨伤损分类算法，其采用Tamura纹理特征与局部二值模式(local binary pattern, LBP)相结合的算法提取伤损B显图像的特征值并组成特征向量，使得作为分类器的支持向量机(support vector machine，SVM)能够对不同种类伤损的特征向量进行训练，从而用训练后的最优分类函数预测未训练过的待测伤损的类别。试验结果表明，所提算法在钢轨伤损图像分类方面实现了较高的分类准确率。

Abstract

There are many types and different shapes of rail defects. Even for the same type of defect, there are differences in the B-Scan images of the ultrasonic rail defect detection software. When the B-Scan image of a certain type of defect changes over a certain range, the detection software cannot identify this type of defect. Therefore, a classification for rail defects based on image processing algorithm was proposed. Firstly, the Tamura texture feature algorithm was combined with the local binary pattern algorithm to extract the feature values of the defect images, and form feature vectors. Secondly, the feature vectors of different kinds of defects were trained by support vector machine, and the optimal classification function was obtained. Finally, the category of untrained defects could be predicted by the optimal classification function. The experimental results showed that the proposed algorithm achieved high accuracy in the classification of rail defect images.

关键词

钢轨Tamura纹理特征LBP特征提取SVM钢轨伤损分类

Keywords

railTamura texture featureLBPfeature extractionSVMrail defect classification

references

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