基于X射线图像与稀疏描述的管道环焊缝缺陷自动识别法  

Research on automatic identification method for pipeline girth weld defects based on X-ray images and sparse representation

作　　者：贾韶辉 李亚平 高炜欣[2] 彭云超 张新建 王玉霞 JIA Shaohui;LI Yaping;GAO Weixin;PENG Yunchao;ZHANG Xinjian;WANG Yuxia(PipeChina Institute of Science and Technology;School of Electronic Engineering,Xi'an Shiyou University;PipeChina Eastern Oil Storageand TransportationCo.Ltd.)

机构地区：[1]国家管网集团科学技术研究总院分公司,河北省廊坊市065000 [2]西安石油大学电子工程学院 [3]国家管网集团东部原油储运有限公司

出　　处：《油气储运》2024年第9期1048-1055,1079,共9页Oil & Gas Storage and Transportation

基　　金：国家管网集团科学研究与技术开发项目“管道大数据分析与应用研究”WZXGL202107;陕西省重点研发计划项目“小径管及长输管道X射线焊缝图像缺陷自动检测系统”,2024GX-YBXM-003。

摘　　要：【目的】环焊缝焊接质量是影响管道安全运行的重要因素,X射线则为焊缝缺陷检测的关键技术之一,但管道环焊缝缺陷及噪声的X射线图像特征值存在难以区分的问题。【方法】提出了一种高准确度的管道环焊缝缺陷X射线图像自动识别方法:在疑似缺陷区域(SuspectedDefectRegion,SDR)与灰度密度定义的基础上,构建了一种基于聚类的SDR分割算法,可以准确分割任意形状的SDR。为保证分割后图像识别的成功率,将判断X射线SDR图像是否为缺陷作为一种模式识别问题处理,并将待检测SDR图像视为样本SDR图像(即字典矩阵)的线性组合,通过求取系数向量判断分割后的SDR图像是否为缺陷。为使系数向量稀疏化方便判断,通过零范数最优化求解系数向量。同时,利用一种光滑可导的0-1惩罚项函数,使采用罚函数方法求零范数最优问题变为可能。为使字典矩阵能够包含尽可能多的图像特征,建立基于正交最优的环焊缝X射线SDR图像最优字典矩阵模型,并提出了一种正交最优字典矩阵求解算法。【结果】基于所建模型及算法,开发了稀疏描述与X射线图像检测技术相结合的管道环焊缝缺陷检测软件,通过对管径762mm、壁厚10.3mm的某管道环焊缝缺陷进行X射线图像识别,缺陷检出率可达98%。【结论】新提出的缺陷识别方法可大幅提升管道环焊缝安全隐患检测的质量与效率,具备工业化应用前景。(图7,表3,参22)[Objective]Girth weld quality is considered a significant factor influencing the safe operation of pipelines.X-ray testing serves as a vital technology for identifying weld defects.Nevertheless,differentiating between the feature values of X-ray images for pipeline girth weld defects and noise poses a considerable challenge.[Methods]This paper proposes a high-accuracy automatic recognition method for X-ray images of pipeline girth weld defects.Based on Suspected Defect Region(SDR)and formulated gray densities,a clustering-based SDR segmentation algorithm was constructed,aimed at precise segmentation of defect SDRs in various shapes.To ensure a high success rate of image recognition after segmentation,judging whether an X-ray SDR image represents a defect was treated as a pattern recognition process.By considering SDR images to be evaluated as a linear combination of sample SDR images(i.e.,dictionary matrix),the approach of deriving coefficient vectors was employed to determine whether the segmented SDR image signifies a defect.To facilitate judgments with sparse coefficient vectors,coefficient vectors were solved by O-norm optimization.In addition,the inclusion of a smooth and differentiable function with 0O-1 penalty terms made it possible to solve O-norm optimization using a penalty function method.To maximize the library of image features within the dictionary matrix,an optimal model was established for X-ray SDR images of welds based on orthogonal optimization,along with a dictionary matrix solving algorithm featuring orthogonal optimization.[Results]The established model and algorithm led to the development of pipeline girth weld defect detection software integrating sparse representation and X-ray image detection technology.This software was used to recognize X-ray images of weld defects for a pipeline with a diameter of 762 mm and a wall thickness of 10.3 mm.The outcomes demonstrated an impressive defect detection rate of up to 98%.[Conclusion]The proposed defect identification method demonstrates its capa

关 键 词：管道 环焊缝 X射线 图像 缺陷识别 稀疏描述 

分 类 号：TE88[石油与天然气工程—油气储运工程]