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管道焊缝数字图像缺陷自动识别技术 预览

Automatic defect identification technology of digital image of pipeline weld
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摘要 管道焊缝数字图像是管道焊缝可靠性管理的重要依据,但对其进行人工判别的误判率较高。为了提高对管道焊缝数字图像缺陷的识别准确度,采用多项边缘检测、检测通道与阈值分割等方法,对管道焊缝图像中存在的缺陷进行图像处理,构造了焊缝数字图像缺陷特征库,包含灰度差、等效面积、圆形度、熵、相关度等参数,建立了多分类器构造(SVM)模型,实现了对管道焊缝数字图像缺陷的分类评价,最终开发出管道焊缝数字图像缺陷自动识别软件,并进行了现场验证分析。研究结果表明:①图像处理后在没有噪声的情况下,Canny等算法都可以得到很好的边缘检测结果,在有噪声的情况下,检测结果出现伪边缘,选用自动选取阈值方法进行图像边缘检测,能够取得合理的阈值;②所建立的焊缝数字图像缺陷特征数据库包含形状特征和纹理特征、图像长度像素等14项参数;③通过所建立的SVM分类模型,可以分类获取缺陷形状特征,找出裂纹、夹渣、气孔、未焊透、未熔合和条形等缺陷特征。现场应用结果表明:①该缺陷自动识别技术适用于对各类管道焊缝缺陷质量的识别判定;②其识别准确率超过90%;③该技术实现了对管道焊缝数字图像缺陷的自动识别和自动化评价。结论认为,该研究成果有助于确保管道的安全运行。 Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy ofdigital image of pipeline weld,we adopted several methods (e.g.multiple edge detection,detection channel and threshold segmentation) to carry out image processing on the image defects of pipeline welds.Then,a defect characteristic database on the digital images of pipeline welds was constructed,including grayscale difference,equivalent area (S/C),circularity,entropy,correlation and other parameters.Furthermore,a multi-classifier construction (SVM) model was established.Thus,the classification and evaluation on the defects in the digital images of pipeline welds were realized.Finally,an automatic defect identification software fordigital image of pipeline weld was developed and verified on site.And the following research results were obtained.First,after image processing,the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise.In the case of noise,however,pseudo-edge emerges in the detection results.In this case,the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold.Second,there are 14 parameters in the defect characteristic database,including shape characteristic,lamination characteristic and image length pixel.Third,by virtue of the SVM classification model,the shape characteristics of each type of defect can be clarified,and the defect characteristics can be identified,such as crack,slag inclusion,air hole,incomplete penetration,non-fusion and strip.Based on field application,the following results were obtained.First,this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds.Second,its identification accuracy is higher than 90%.Third,by virtue of this technology,automatic defect identification and evaluation
作者 董绍华 孙玄 谢书懿 王明锋 Dong Shaohua;Sun Xuan;Xie Shuyi;Wang Mingfeng(College of Mechanical and Transportation Engineering,China University of Petroleum <Beijing>,Beijing 102249,China;China University of Petroleum-Beijing at Karamay,Karamay,Xingjiang 834000,China;Operation Districtof Hutubi Gas Storage,PetroChina Xinjiang Oilfield Company,Hutubi,Xingjiang 831200,China)
出处 《天然气工业》 EI CAS CSCD 北大核心 2019年第1期113-117,共5页 Natural Gas Industry
基金 国家重点研发计划项目“国家石油及天然气储备库安全保障技术与装备研发”(编号:2017YFC0805800) 国家自然科学基金项目“X80管线钢氢损伤失效行为与完整性评价模型研究”(编号:51874322)。
关键词 管道焊缝 射线底片 数字图像 缺陷库 SVM分类模型 缺陷识别 自动识别 软件开发 Pipeline weld Ray film Digital image Defect database SVM classification model Defect identification Automatic identification Software development
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