管道焊缝数字图像缺陷自动识别技术
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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 of digital image of pipeline weld is realized. In conclusion, these research results help to ensure the safe operation of pipelines.
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 of digital image of pipeline weld is realized. In conclusion, these research results help to ensure the safe operation of pipelines.
引文
[1]吴宏,周剑琴.国内大口径、高钢级管道焊接及焊缝检测技术现状[J].油气储运,2017,36(1):21-27.Wu Hong&Zhou Jianqin.Current status of welding and weld testing technologies for large-diameter high-grade pipelines in China[J].Oil&Gas Storage and Transportation,2017,36(1):21-27.
[2]董绍华.管道完整性评估理论与应用[M].北京:石油工业出版社,2014.Dong Shaohua.Pipeline integrity assessment theory and application[M].Beijing:Petroleum Industry Press,2014.
[3]Kushwaha AKS,Srivastava S&Srivastava R.Multi-view human activity recognition based on silhouette and uniform rotation invariant local binary patterns[J].Multimedia Systems,2016,23(4):1-17.
[4]Ojala T,Pietikaainen M&Harwood D.Acomparative study of texture measures with classification based on featured distributions[J].Pattern Recognition,1996,29(1):51-59.
[5]周道川,王艳婷.基于BS 7910的焊缝缺陷剩余寿命分析方法[J].油气储运,2016,35(1):78-85.Zhou Daochuan&Wang Yanting.Residual life analysis method of weld seam with defects based on BS 7910[J].Oil&Gas Storage and Transportation,2016,35(1):78-85.
[6]Kasban H,Zahran O,Arafa H,El-Kordy M,Elaraby SMS&Abd El-Samie FE.Welding defect detection from radiography images with a cepstral approach[J].NDT&E International,2011,44(2):226-231.
[7]Halim SA,Hadi NA,Ibrahim A&Manurung YH.The geometrical feature of weld defect in assessing digital radiographic image[C]//2011 IEEE International Conference on Imaging Systems and Techniques,17-18 May 2011,Penang,Malaysia.
[8]Ojala T,Pietik?inen M&M?enp??TT.Multiresolution grayscale and rotation invarianttextureclassification with local binary pattern[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(9):971-987.
[9]Ojala T,Pietikaainen M&Harwood D.A comparative study of texture measures with classification based on featured distributions[J].Pattern Recognition,1996,29(1):51-59.
[10]Guo Zhenhua&Zhang Lei.A completed modeling of local binary pattern operator for texture classification[J].IEEE Transactions on Image Processing,2010,19(6):1657-1663.
[11]Tian Yuan,Du Dong,Hou Runshi,Gao Zhiling&Shen Liqun.Automatic defect detection in X-ray images using image data fusion[J].Tsinghua Science&Technology,2006,11(6):720-724.
[12]Felisberto MK,Lopes HS&Centeno TM.An object detection and recognition system for weld bead extraction from digital radiographs[J].Computer Vision and Image Understanding,2006,102(3):238-249.
[13]Lecomte G&Kaftandjian V.A robust segmentation approach based on features analysis for defect detection in aluminium castings X-ray images[J].Non Destructive Testing and Condition Monitoring,2007,49(10):572-577.
[14]Lim TY,Ratnam MM&Khalid MA.Automatic classification of weld defects using simulated data and an MLP neural network[J].Non Destructive Testing and Condition Monitoring,2007,49(3):154-159.
[15]Juan Zapata,Rafael Vilar&Ramón Ruiz.Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers[J].Expert Systems with Applications,2011,38(7):8812-8824.
[16]Zhang Xiaoguang,Ren Shijin,Xu Jihua&Zhu Zhencai.The classification algorithm of defects in weld image based on asymmetrical SVMs[C]//Proceedings of International Conference on Control and Automation,26-29 June 2005,Budapest,Hungary.
[17]Alaknanda,Anand RS&Kumar P.Flaw detection in radiographic weld images using morphological approach[J].NDT&E International,2006,39(1):29-33.
[2]董绍华.管道完整性评估理论与应用[M].北京:石油工业出版社,2014.Dong Shaohua.Pipeline integrity assessment theory and application[M].Beijing:Petroleum Industry Press,2014.
[3]Kushwaha AKS,Srivastava S&Srivastava R.Multi-view human activity recognition based on silhouette and uniform rotation invariant local binary patterns[J].Multimedia Systems,2016,23(4):1-17.
[4]Ojala T,Pietikaainen M&Harwood D.Acomparative study of texture measures with classification based on featured distributions[J].Pattern Recognition,1996,29(1):51-59.
[5]周道川,王艳婷.基于BS 7910的焊缝缺陷剩余寿命分析方法[J].油气储运,2016,35(1):78-85.Zhou Daochuan&Wang Yanting.Residual life analysis method of weld seam with defects based on BS 7910[J].Oil&Gas Storage and Transportation,2016,35(1):78-85.
[6]Kasban H,Zahran O,Arafa H,El-Kordy M,Elaraby SMS&Abd El-Samie FE.Welding defect detection from radiography images with a cepstral approach[J].NDT&E International,2011,44(2):226-231.
[7]Halim SA,Hadi NA,Ibrahim A&Manurung YH.The geometrical feature of weld defect in assessing digital radiographic image[C]//2011 IEEE International Conference on Imaging Systems and Techniques,17-18 May 2011,Penang,Malaysia.
[8]Ojala T,Pietik?inen M&M?enp??TT.Multiresolution grayscale and rotation invarianttextureclassification with local binary pattern[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(9):971-987.
[9]Ojala T,Pietikaainen M&Harwood D.A comparative study of texture measures with classification based on featured distributions[J].Pattern Recognition,1996,29(1):51-59.
[10]Guo Zhenhua&Zhang Lei.A completed modeling of local binary pattern operator for texture classification[J].IEEE Transactions on Image Processing,2010,19(6):1657-1663.
[11]Tian Yuan,Du Dong,Hou Runshi,Gao Zhiling&Shen Liqun.Automatic defect detection in X-ray images using image data fusion[J].Tsinghua Science&Technology,2006,11(6):720-724.
[12]Felisberto MK,Lopes HS&Centeno TM.An object detection and recognition system for weld bead extraction from digital radiographs[J].Computer Vision and Image Understanding,2006,102(3):238-249.
[13]Lecomte G&Kaftandjian V.A robust segmentation approach based on features analysis for defect detection in aluminium castings X-ray images[J].Non Destructive Testing and Condition Monitoring,2007,49(10):572-577.
[14]Lim TY,Ratnam MM&Khalid MA.Automatic classification of weld defects using simulated data and an MLP neural network[J].Non Destructive Testing and Condition Monitoring,2007,49(3):154-159.
[15]Juan Zapata,Rafael Vilar&Ramón Ruiz.Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers[J].Expert Systems with Applications,2011,38(7):8812-8824.
[16]Zhang Xiaoguang,Ren Shijin,Xu Jihua&Zhu Zhencai.The classification algorithm of defects in weld image based on asymmetrical SVMs[C]//Proceedings of International Conference on Control and Automation,26-29 June 2005,Budapest,Hungary.
[17]Alaknanda,Anand RS&Kumar P.Flaw detection in radiographic weld images using morphological approach[J].NDT&E International,2006,39(1):29-33.
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