Segmenting welding flaws of non-horizontal shape

Alexandria Engineering Journal - Tập 60 - Trang 4057-4065 - 2021
Doaa Radi1, Mohy Eldin A Abo-Elsoud1, Fahmi Khalifa1
1Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Dakahlyia, 35516, Egypt

Tài liệu tham khảo

Zhang, 2019, Weld defect detection based on deep learning method, IEEE Int. Conf. Autom. Sci. Eng., IEEE Computer Society, 1574 Valavanis, 2010, Multiclass defect detection and classification in weld radiographic images using geometric and texture features, Expert Syst. Appl., 37, 7606, 10.1016/j.eswa.2010.04.082 Ben Gharsallah, 2015, Weld inspection based on radiography image segmentation with level set active contour guided off-center saliency map, Adv. Mater. Sci. Eng., 10.1155/2015/871602 Zahran, 2013, Automatic weld defect identification from radiographic images, NDT E Int., 10.1016/j.ndteint.2012.11.005 X. Dang, D. Li, Edge Detection of Welding Seam Image Based on Two-dimensional Fuzzy Entropy and Genetic Algorithm, (2015) 703–706. https://doi.org/10.2991/isrme-15.2015.140. Halim, 2013, PDE-based model for weld defect detection on digital radiographic image, Int. J. Signal Process. Syst., 1, 146, 10.12720/ijsps.1.2.146-151 L. Gaohua, X. Junmei, Image processing technology for pipe weld visual inspection, in: 2009 WASE Int. Conf. Inf. Eng. ICIE 2009, 2009. https://doi.org/10.1109/ICIE.2009.262. A. Azarimoghaddam, L. Rangarajan, A Method for Segmentation Radiographic Images with Case Study on Welding Defects, 2012 (n.d.) 277–283. https://doi.org/10.1007/978-81-322-1000-9. S. Dubey, K. Shah, Analysis of V arious F laws d etection using, 3 (2012) 765–774. A. Goumeidane, A. Bouzaieni, N. Nacereddine, I. Industrial, S.A. Tabbone, Bayesian Networks-Based Defects Classes Discrimination in Weld Radiographic Images Bayesian Networks-Based Defects Classes Discrimination in Weld Radiographic Images, (2015). https://doi.org/10.1007/978-3-319-23117-4. Chu, 2016, A vision-based system for post-welding quality measurement and defect detection, Int. J. Adv. Manuf. Technol., 3007, 10.1007/s00170-015-8334-1 W. Hou, D. Zhang, Y. Wei, J. Guo, X. Zhang, applied sciences Review on Computer Aided Weld Defect Detection from Radiography Images, (2020) 1–16. https://doi.org/10.3390/app10051878. Dong, 2019, ScienceDirect, Nat. Gas Ind. B., 6, 399, 10.1016/j.ngib.2019.01.016 Yahia, 2011, Automatic detection of welding defects using radiography with a neural approach, Procedia Eng., 10, 671, 10.1016/j.proeng.2011.04.112 Hassan, 2012, Welding defect detection and classification using geometric features, Proc. - 10th Int Conf. Front. Inf. Technol. FIT, 139 D. Mery, Automated detection of welding defects without segmentation, 4860, 2005. D.W. Aha, R.L. Bankert, A Comparative Evaluation of Sequential Feature Selection Algorithms, 1996. https://doi.org/10.1007/978-1-4612-2404-4_19. Baniukiewicz, 2014, Radiography, 327 A.B. Goumeidane, M. Khamadja, N. Nacereddine, Adaptive and Statistical Polygonal Curve for Multiple Weld Defects Detection in Radiographic Images Adaptive and Statistical Polygonal Curve for Multiple Weld Defects Detection in Radiographic Images, 2011. Akgül, 2018, A novel method for a fractional derivative with non-local and non-singular kernel, Chaos, Solitons Fractals, 114, 478, 10.1016/j.chaos.2018.07.032 Akgül, 2019, Crank-Nicholson difference method and reproducing kernel function for third order fractional differential equations in the sense of Atangana-Baleanu Caputo derivative, Chaos Solitons and Fractals, 10.1016/j.chaos.2019.06.011 Karatas, 2019, Solutions of the linear and nonlinear differential equations within the generalized fractional derivatives, Chaos An Interdiscipl. J. Nonlinear Sci. A. Akgül, A. Fernandez, On a Fractional Operator Combining Proportional and Classical Differintegrals, (n.d.) 1–13. https://doi.org/10.3390/math8030360. Akgül, 2020, Solutions of fractional gas dynamics equation by a new technique, Math. Methods Appl. Sci., 10.1002/mma.5950 Akgül, 2019, Reproducing kernel Hilbert space method based on reproducing kernel functions for investigating boundary layer flow of a Powell-Eyring non-Newtonian fluid, J. Taibah Univ. Sci., 13, 858, 10.1080/16583655.2019.1651988 R. Sikora, T. Chady, B. Grzywacz, On the possibility of use of fractional order derivatives to eddy current non-destructive testing, in: Proc. 2017 IEEE Far East NDT New Technol. Appl. Forum, FENDT 2017, 2018. https://doi.org/10.1109/FENDT.2017.8584541. S. Chakraborty, R. Acharya, R.K. Gupta, V.K. Mishra, Computer Aided Defect Detection Strategy for Welded Joints ., (2016) 104–109. Mery, 2015, GDXray: the database of X-ray images for nondestructive testing, J. Nondestruct. Eval., 34, 1, 10.1007/s10921-015-0315-7 Hou, 2018, Automatic detection of welding defects using deep neural network, J. Phys. Conf. Ser., 933, 10.1088/1742-6596/933/1/012006 Thirugnanam, 2014, Evaluating the performance of various segmentation techniques in industrial radiographs, Cybern. Inf. Technol., 14, 161 Y. Aslam, N. Santhi, K. Ramar, Literature Survey on Various Types of Surface Defect Detection Techniques, 10 (2017) 233–242. M. Piccardi, Background subtraction techniques: a review, Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern. 4 (2004) 3099–3104. https://doi.org/10.1109/ICSMC.2004.1400815. Li, 2006, Improving automatic detection of defects in castings by applying wavelet technique, IEEE Trans. Ind. Electron., 53, 1927, 10.1109/TIE.2006.885448 Lashkia, 2001, Defect detection in X-ray images using fuzzy reasoning, Image Vis. Comput., 19, 261, 10.1016/S0262-8856(00)00075-5 Weimer, 2013, Learning defect classifiers for textured surfaces using neural networks and statistical feature representations, Procedia CIRP, 10.1016/j.procir.2013.05.059 J. Zapata, R. Vilar, R. Ruiz, Automatic Inspection System of Welding Radiographic Images Based on ANN Under a Regularisation Process, (2012) 34–45. https://doi.org/10.1007/s10921-011-0118-4. Zhao, 2019, Deep learning and its applications to machine health monitoring, Mech. Syst. Signal Process. Ren, 2018, A generic deep-learning-based approach for automated surface inspection, IEEE Trans. Cybern., 10.1109/TCYB.2017.2668395 Ren, 2017, Faster R-CNN: towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 10.1109/TPAMI.2016.2577031 Kolar, 2018, Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images, Autom. Constr., 10.1016/j.autcon.2018.01.003 J. Kumar, R.S. Anand, S.P. Srivastava, Flaws classification using ANN for radiographic weld images, in: 2014 Int. Conf. Signal Process. Integr. Networks, SPIN 2014, 2014. https://doi.org/10.1109/spin.2014.6776938. C. Tomography, I. Applications, I. Processing, R. March, G. Dgzfp, P. Bb, C.D. Paper, Automated Evaluation of Digitized Radiographs with Neuronal Methods C. Jacobsen, U. Zscherpel, BAM, Berlin, D, (1999) 141–152. Feng, 2019, Using deep neural network with small dataset to predict material defects, Mater. Des., 162, 300, 10.1016/j.matdes.2018.11.060 Boaretto, 2017, Automated detection of welding defects in pipelines from radiographic images DWDI, NDT E Int., 10.1016/j.ndteint.2016.11.003 F. Mirzaei, M. Faridafshin, A. Movafeghi, R. Faghihi, Automated Defect Detection of Weldments and Castings using Canny , Sobel and Gaussian filter Edge Detectors : A Comparison Study, 2017.