Review on Computer Aided Weld Defect Detection from Radiography Images
The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of...
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| Published in | Applied sciences Vol. 10; no. 5; p. 1878 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Basel
MDPI AG
01.03.2020
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| Abstract | The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers. |
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| AbstractList | The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers. |
| Author | Wei, Ye Zhang, Xiaolong Hou, Wenhui Guo, Jie Zhang, Dashan |
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| Cites_doi | 10.1016/S0963-8695(02)00025-7 10.1088/1742-6596/1314/1/012064 10.3390/app9235064 10.1016/j.jmatprotec.2017.03.031 10.1016/j.eswa.2011.01.092 10.1016/j.jsv.2014.04.062 10.1038/nature14539 10.1016/j.ndteint.2008.07.004 10.1109/TIE.2019.2896165 10.1016/j.jmsy.2019.02.004 10.1016/S0963-8695(97)00042-X 10.1016/j.ndteint.2003.10.003 10.1109/34.574797 10.1016/j.ndteint.2012.11.005 10.1784/insi.2010.52.3.134 10.1016/j.ymssp.2018.05.050 10.1016/j.ndteint.2003.10.004 10.1007/s10921-015-0305-9 10.1063/1.5048766 10.1109/TPAMI.1986.4767851 10.1016/j.ndteint.2010.10.005 10.1137/1031128 10.1023/B:JIMS.0000010076.56537.07 10.1016/j.autcon.2018.07.008 10.1109/ICAEE.2014.6838443 10.1016/j.ndteint.2004.10.007 10.1080/09507119009447695 10.1016/S0020-0255(00)00016-5 10.1016/S0165-1684(02)00158-5 10.1109/TMI.2004.824224 10.1784/insi.45.7.475.54452 10.1038/381607a0 10.1023/B:VISI.0000022288.19776.77 10.1109/MSP.2010.939537 10.1016/j.ndteint.2005.05.005 10.1109/ICInfA.2014.6932710 10.1111/mice.12263 10.1016/j.ndteint.2017.11.006 10.1016/j.ndteint.2016.11.003 10.1016/j.ndteint.2003.12.004 10.1111/str.12336 10.1016/S0957-4174(03)00010-1 10.1016/j.eswa.2010.04.082 10.1016/j.ndteint.2009.02.004 10.1016/j.measurement.2018.09.011 10.1109/SPIN.2014.6776938 10.1016/j.jmapro.2019.06.023 10.1016/j.eswa.2007.08.044 10.1784/insi.45.10.676.52952 10.1016/S0167-8655(00)00098-2 10.2355/isijinternational.39.1081 10.1088/1742-6596/1237/3/032005 10.1002/9781118646106 10.1007/978-981-13-8331-1_11 10.1016/S0165-0114(97)00307-2 |
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| References | Nguyen (ref_55) 2018; 94 Mirapeix (ref_47) 2009; 42 Li (ref_13) 2017; 246 Zhang (ref_59) 2019; 45 ref_14 ref_57 ref_12 ref_54 Zahran (ref_11) 2013; 57 Valavanis (ref_29) 2010; 37 Civera (ref_65) 2020; 56 Liao (ref_19) 1998; 31 Mery (ref_48) 2003; Volume 45 ref_15 Jain (ref_46) 1997; 19 Shafeek (ref_2) 2004; 37 ref_61 Aoki (ref_10) 1999; 39 Balsamo (ref_43) 2014; 333 Ye (ref_16) 2019; 1314 Felzenszwalb (ref_28) 2004; 59 Kasban (ref_45) 2011; 44 ref_25 Civera (ref_42) 2019; 9 ref_67 Cha (ref_56) 2017; 32 ref_20 Murakami (ref_23) 1990; 4 ref_64 Hyatt (ref_17) 1996; 54 Mery (ref_41) 2003; Volume 45 LeCun (ref_51) 2015; 521 Liao (ref_66) 2008; 35 Zhang (ref_58) 2019; 51 Carrasco (ref_21) 2004; 62 Siqueira (ref_36) 2004; 37 Anand (ref_27) 2006; 39 Liao (ref_31) 2000; 126 Zhao (ref_50) 2019; 115 Zapata (ref_6) 2011; 38 Perner (ref_33) 2001; 22 Boaretto (ref_34) 2017; 86 Vilar (ref_49) 2009; 42 Chen (ref_60) 2018; 94 ref_44 Yaping (ref_62) 2019; 1237 Canny (ref_26) 1986; 8 ref_40 ref_1 ref_3 Liao (ref_35) 2003; 25 Kazantsev (ref_22) 2002; 82 Liao (ref_32) 2004; 15 Daum (ref_18) 1987; 29 Shafeek (ref_39) 2004; 37 Olshausen (ref_53) 1996; 381 Hou (ref_63) 2019; 131 Liao (ref_30) 1999; 108 Tosic (ref_52) 2011; 28 Grau (ref_24) 2004; 23 Strang (ref_8) 1989; 31 Wang (ref_9) 2002; 35 Shen (ref_38) 2010; Volume 52 ref_5 ref_4 ref_7 Siqueira (ref_37) 2005; 38 |
| References_xml | – volume: 35 start-page: 519 year: 2002 ident: ref_9 article-title: Automatic identification of different types of welding defects in radiographic images publication-title: NDT E Int. doi: 10.1016/S0963-8695(02)00025-7 – volume: 1314 start-page: 012064 year: 2019 ident: ref_16 article-title: Detection and recognition of defects in X-ray images of welding seams under compressed sensing publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/1314/1/012064 – volume: 9 start-page: 5064 year: 2019 ident: ref_42 article-title: The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool publication-title: Appl. Sci. doi: 10.3390/app9235064 – volume: 246 start-page: 285 year: 2017 ident: ref_13 article-title: Welding quality monitoring of high frequency straight seam pipe based on image feature publication-title: J. Mater. Process. Technol. doi: 10.1016/j.jmatprotec.2017.03.031 – ident: ref_5 – volume: 38 start-page: 8812 year: 2011 ident: ref_6 article-title: Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.01.092 – volume: 333 start-page: 4526 year: 2014 ident: ref_43 article-title: A structural health monitoring strategy using cepstral features publication-title: J. Sound Vib. doi: 10.1016/j.jsv.2014.04.062 – volume: 521 start-page: 436 year: 2015 ident: ref_51 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 42 start-page: 56 year: 2009 ident: ref_47 article-title: Spectral processing technique based on feature selection and artificial neural networks for arc-welding quality monitoring publication-title: NDT E Int. doi: 10.1016/j.ndteint.2008.07.004 – ident: ref_57 doi: 10.1109/TIE.2019.2896165 – volume: 51 start-page: 87 year: 2019 ident: ref_58 article-title: Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of thick plates publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2019.02.004 – volume: 31 start-page: 183 year: 1998 ident: ref_19 article-title: An automated radiographic NDT system for weld inspection: Part II—Flaw detection publication-title: NDT E Int. doi: 10.1016/S0963-8695(97)00042-X – ident: ref_1 – volume: 37 start-page: 291 year: 2004 ident: ref_2 article-title: Assessment of welding defects for gas pipeline radiographs using computer vision publication-title: NDT E Int. doi: 10.1016/j.ndteint.2003.10.003 – volume: 19 start-page: 153 year: 1997 ident: ref_46 article-title: Feature selection: Evaluation, application, and small sample performance publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.574797 – volume: 57 start-page: 26 year: 2013 ident: ref_11 article-title: Automatic weld defect identification from radiographic images publication-title: NDT E Int. doi: 10.1016/j.ndteint.2012.11.005 – volume: Volume 52 start-page: 134 year: 2010 ident: ref_38 article-title: Automatic classification of weld defects in radiographic images publication-title: Insight—Non-Destructive Testing and Condition Monitoring doi: 10.1784/insi.2010.52.3.134 – volume: 115 start-page: 213 year: 2019 ident: ref_50 article-title: Deep learning and its applications to machine health monitoring publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2018.05.050 – volume: 37 start-page: 301 year: 2004 ident: ref_39 article-title: Automatic inspection of gas pipeline welding defects using an expert vision system publication-title: NDT E Int. doi: 10.1016/j.ndteint.2003.10.004 – ident: ref_14 doi: 10.1007/s10921-015-0305-9 – ident: ref_61 doi: 10.1063/1.5048766 – volume: 8 start-page: 679 year: 1986 ident: ref_26 article-title: A computational approach to edge detection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.1986.4767851 – ident: ref_4 – volume: 44 start-page: 226 year: 2011 ident: ref_45 article-title: Welding defect detection from radiography images with a cepstral approach publication-title: NDT E Int. doi: 10.1016/j.ndteint.2010.10.005 – volume: 31 start-page: 614 year: 1989 ident: ref_8 article-title: Wavelets and dilation equations: A brief introduction publication-title: SIAM Rev. doi: 10.1137/1031128 – volume: 15 start-page: 69 year: 2004 ident: ref_32 article-title: Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition publication-title: J. Intell. Manuf. doi: 10.1023/B:JIMS.0000010076.56537.07 – volume: 94 start-page: 203 year: 2018 ident: ref_55 article-title: Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network publication-title: Autom. Constr. doi: 10.1016/j.autcon.2018.07.008 – ident: ref_15 doi: 10.1109/ICAEE.2014.6838443 – volume: 38 start-page: 335 year: 2005 ident: ref_37 article-title: Estimated accuracy of classification of defects detected in welded joints by radiographic tests publication-title: NDT E Int. doi: 10.1016/j.ndteint.2004.10.007 – volume: 4 start-page: 144 year: 1990 ident: ref_23 article-title: Image processing for non-destructive testing publication-title: Weld. Int. doi: 10.1080/09507119009447695 – volume: 126 start-page: 21 year: 2000 ident: ref_31 article-title: Extraction of welds from radiographic images using fuzzy classifiers publication-title: Inf. Sci. doi: 10.1016/S0020-0255(00)00016-5 – volume: 82 start-page: 791 year: 2002 ident: ref_22 article-title: Statistical detection of defects in radiographic images in nondestructive testing publication-title: Signal Process. doi: 10.1016/S0165-1684(02)00158-5 – ident: ref_20 – volume: 23 start-page: 447 year: 2004 ident: ref_24 article-title: Improved watershed transform for medical image segmentation using prior information publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2004.824224 – volume: Volume 45 start-page: 475 year: 2003 ident: ref_48 article-title: Pattern recognition in the automatic inspection of aluminium castings publication-title: Insight—Non-Destructive Testing and Condition Monitoring doi: 10.1784/insi.45.7.475.54452 – volume: 381 start-page: 607 year: 1996 ident: ref_53 article-title: Emergence of simple-cell receptive field properties by learning a sparse code for natural images publication-title: Nature doi: 10.1038/381607a0 – volume: 59 start-page: 167 year: 2004 ident: ref_28 article-title: Efficient graph-based image segmentation publication-title: Int. J. Comput. Vis. doi: 10.1023/B:VISI.0000022288.19776.77 – ident: ref_7 – volume: 28 start-page: 27 year: 2011 ident: ref_52 article-title: Dictionary learning: What is the right representation for my signal? publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2010.939537 – ident: ref_3 – volume: 62 start-page: 1142 year: 2004 ident: ref_21 article-title: Segmentation of welding defects using a robust algorithm publication-title: Mater. Eval. – volume: 39 start-page: 29 year: 2006 ident: ref_27 article-title: Flaw detection in radiographic weld images using morphological approach publication-title: Ndt E Int. doi: 10.1016/j.ndteint.2005.05.005 – ident: ref_54 doi: 10.1109/ICInfA.2014.6932710 – volume: 32 start-page: 361 year: 2017 ident: ref_56 article-title: Deep learning-based crack damage detection using convolutional neural networks publication-title: Comput.-Aided Civ. Infrastruct. Eng. doi: 10.1111/mice.12263 – volume: 94 start-page: 62 year: 2018 ident: ref_60 article-title: Accurate defect detection via sparsity reconstruction for weld radiographs publication-title: NDT E Int. doi: 10.1016/j.ndteint.2017.11.006 – volume: 86 start-page: 7 year: 2017 ident: ref_34 article-title: Automated detection of welding defects in pipelines from radiographic images DWDI publication-title: NDT E Int. doi: 10.1016/j.ndteint.2016.11.003 – volume: 37 start-page: 461 year: 2004 ident: ref_36 article-title: Pattern recognition of weld defects detected by radiographic test publication-title: NDT E Int. doi: 10.1016/j.ndteint.2003.12.004 – volume: 54 start-page: 379793 year: 1996 ident: ref_17 article-title: A method for defect segmentation in digital radiographs of pipeline girth welds publication-title: Mater. Eval. – ident: ref_40 – volume: 29 start-page: 79 year: 1987 ident: ref_18 article-title: Automatic recognition of weld defects in x-ray inspection publication-title: Br. J. Nondestruct. Test. – volume: 56 start-page: e12336 year: 2020 ident: ref_65 article-title: An experimental study of the feasibility of phase-based video magnification for damage detection and localisation in operational deflection shapes publication-title: Strain doi: 10.1111/str.12336 – volume: 25 start-page: 101 year: 2003 ident: ref_35 article-title: Classification of welding flaw types with fuzzy expert systems publication-title: Expert Syst. Appl. doi: 10.1016/S0957-4174(03)00010-1 – volume: 37 start-page: 7606 year: 2010 ident: ref_29 article-title: Multiclass defect detection and classification in weld radiographic images using geometric and texture features publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.04.082 – volume: 42 start-page: 467 year: 2009 ident: ref_49 article-title: An automatic system of classification of weld defects in radiographic images publication-title: NDT E Int. doi: 10.1016/j.ndteint.2009.02.004 – volume: 131 start-page: 482 year: 2019 ident: ref_63 article-title: Deep Features Based A DCNN Model Classifying Imbalanced Weld Flaw Types publication-title: Measurement doi: 10.1016/j.measurement.2018.09.011 – ident: ref_25 – ident: ref_12 doi: 10.1109/SPIN.2014.6776938 – volume: 45 start-page: 208 year: 2019 ident: ref_59 article-title: Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding publication-title: J. Manuf. Process. doi: 10.1016/j.jmapro.2019.06.023 – volume: 35 start-page: 1041 year: 2008 ident: ref_66 article-title: Classification of weld flaws with imbalanced class data publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2007.08.044 – volume: Volume 45 start-page: 676 year: 2003 ident: ref_41 article-title: Automatic detection of welding defects using texture features publication-title: Insight—Non-Destructive Testing and Condition Monitoring doi: 10.1784/insi.45.10.676.52952 – volume: 22 start-page: 47 year: 2001 ident: ref_33 article-title: A comparison between neural networks and decision trees based on data from industrial radiographic testing publication-title: Pattern Recognit. Lett. doi: 10.1016/S0167-8655(00)00098-2 – volume: 39 start-page: 1081 year: 1999 ident: ref_10 article-title: Application of artificial neural network to discrimination of defect type in automatic radiographic testing of welds publication-title: ISIJ Int. doi: 10.2355/isijinternational.39.1081 – volume: 1237 start-page: 032005 year: 2019 ident: ref_62 article-title: Research on X-ray welding image defect detection based on convolution neural network publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/1237/3/032005 – ident: ref_64 – ident: ref_67 doi: 10.1002/9781118646106 – ident: ref_44 doi: 10.1007/978-981-13-8331-1_11 – volume: 108 start-page: 145 year: 1999 ident: ref_30 article-title: Detection of welding flaws from radiographic images with fuzzy clustering methods publication-title: Fuzzy Sets Syst. doi: 10.1016/S0165-0114(97)00307-2 |
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| Snippet | The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human... |
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| SubjectTerms | Automation Classification classifier Deep learning defect detection Digitization feature extraction Fuzzy sets image processing Methods Noise Quality radiographic image Radiography Researchers |
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| Title | Review on Computer Aided Weld Defect Detection from Radiography Images |
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