摘要
基于局部二值模式的深度挖掘算法和多特征融合算法是提取铁路隧道漏缆卡扣特征的有效方法,但它们存在描述子表述性不强且特征维度过高的问题。提出分层连续梯度二值模式,能够实现卡扣轮廓特征的尺度变换并降低描述子的特征维度,提高故障卡扣图像的分类准确率。首先采用改进的中心对称局部二值模式和根据全局灰度均值获得的自适应阈值,计算采样圆域的梯度方向特征,得到完整的初步梯度方向特征图;然后在此特征图上进行两次连续的下采样迭代,并分别提取这两幅下采样特征图的连续梯度特征;最后,将这两层不同尺度的连续梯度特征串联作为描述子,用支持向量机完成漏缆卡扣图像的故障检测任务。实验结果表明,本文所提算法的召回率和精准度分别达到了0.923和0.857,相较于局部二值模式、中心对称局部二值模式、以及该系列的多种变体算法有明显的优势。
Deep mining algorithms and multi-feature fusion algorithms based on local binary patterns are effective methods for extracting the fixture features of leaky cables in railway tunnels;however,there are disadvantages that the descriptors are not expressive enough and that their feature dimensions are too high.In this paper,layered continuous gradient local binary pattern(LCG-LBP)was proposed,which could realize the scale transformation of leaky cable fixture features.It could reduce the feature dimension of the fusion descriptor extracted from down-sampling feature maps.It could also improve the classification accuracy of faulty fixture images effectively.First,the improved algorithm based on center-symmetric local binary pattern(CS-LBP)and the adaptive threshold obtained by the global gray average value were used to calculate the gradient direction feature in a circle domain unit,and the complete preliminary gradient direction feature map was obtained in this way.Then,two consecutive down-sampling iterations were performed on this preliminary feature map to obtain two down-sampling feature maps,and the continuous gradient features were extracted from these two down-sampling feature maps.Finally,the two layers of continuous gradient features in different scales were connected in series as a fusion descriptor,and a support vector machine(SVM)was used to complete the defect detection process using faulty cable fixture images obtained from railway tunnels.The experimental results show that the recall and accuracy of the algorithm proposed in this paper are 0.923 and 0.857,respectively,which show that the proposed algorithm has obvious advantages compared with local binary pattern(LBP),CS-LBP,and other variants.
作者
张云佐
宋洲臣
郭威
董旭
ZHANG Yunzuo;SONG Zhouchen;GUO Wei;DONG Xu(School of Information Science and Technology,Shijiazhuang Tiedao University,Shijiazhuang 050043,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2022年第3期331-339,共9页
Optics and Precision Engineering
基金
广东省重点领域研发计划资助项目(No.2019B010137002)
国家自然科学基金资助项目(No.61702347,No.61972267,No.62027801)
河北省自然科学基金资助项目(No.F2017210161)
河北省教育厅科学研究项目(No.ZD2016052)。
关键词
故障检测
漏缆卡扣
尺度变换
连续梯度
局部二值模式
defect detection
leaky cable fixture
scale transformation
continuous gradient
local binary pattern
