Machine Learning-Based Welding Defect Recognition Using GLCM Features and k-Fold Cross-Validation: KNN and SVM Techniques

Downloads

Authors

  • Sattar J. Kadhim University of Basrah, Iraq
  • AbdulBaqi AlSalait Consultant of Energy Affairs, Ministry of Oil, Government of Iraq, Iraq
  • Raheem Al-Sabur University of Basrah, Iraq ORCID ID 0000-0003-1012-7681
  • Abdel-Nasser Sharkawy Qena University, Egypt / Fahad Bin Sultan University, Saudi Arabia ORCID ID 0000-0001-9733-221X

Abstract

Although radiographic inspection is one of the oldest techniques for non-destructive testing, it is still considered vital in many industrial fields to ensure the quality of welds and meet the demands of work conditions and design, as well as safety and reliability requirements. This paper presents an algorithm that identifies and categorizes welding defects in radiographic images using machine learning techniques. For this aim, two supervised classifiers are proposed and conducted: 1) K-nearest neighbor (KNN), which is a nonparametric classifier, and 2) a multiclass classifier based on a support vector machine (SVM) as a highly generalized learning-based classifier. SVM is commonly used in binary classification, but it can be adapted for multi-classification using various common methods, such as one-versus-one and one-against-all. The texture features are adopted in this paper as inputs to the classifiers, where two groups of them are used: the local binary pattern (LBP) features and the gray-level co-occurrence matrix (GLCM) extraction, to obtain the feature vector. To avoid the risk of overfitting, a 4-fold cross-validation is applied. The experimental results are reported for the two different classifiers, achieving an accuracy of 91.66 % when combining GLCM and SVM.

Keywords:

welding defect, radiographic images, classification, machine learning, k-fold cross-validation

References


  1. Kumar D.D., Fang C., Zheng Y., Gao Y., Semi-supervised transfer learning-based automatic weld defect detection and visual inspection, Engineering Structures, 292: 116580, 2023, https://doi.org/10.1016/j.engstruct.2023.116580

  2. Kadhim S.J., Al-Sabur R.K., Ali A.B.K., Application of different median filter algorithms for welding defects clarification in radiographic images, University of Thi-Qar Journal for Engineering Sciences, 11(1): 56–61, 2020, https://doi.org/10.31663/tqujes.11.1.378(2020)

  3. Wang D., Yi Q., Liu Y., Lu R., Tian G., Advanced detection and reconstruction of welding defects in irregular geometries using eddy current pulsed thermography, NDT & E International, 154: 103398, 2025, https://doi.org/10.1016/j.ndteint.2025.103398

  4. Alvarado J.W.V., Garcia L.F.C., Neira M.T., Flores J.W.V., Probability of defects detection in welded joints using the magnetic particle method, Archives of Metallurgy and Materials, 69(2): 607–612, 2024, https://doi.org/10.24425/amm.2024.149789

  5. Sumardani N.I., Setiawan N.I., Nuryadin B.W., Sumardani D., Defect analysis of carbonsteel pipe welding connections using non-destructive testing with the penetrant test method, Risenologi, 5(1): 38–47, 2020, https://doi.org/10.47028/j.risenologi.2020.51.72

  6. Aboali A., El-Shaib M., Sharara A., Shehadeh M., Screening for welding defects using acoustic emission technique, Advanced Materials Research, 1025–1026: 7–12, 2014, https://doi.org/10.4028/www.scientific.net/amr.1025-1026.7

  7. Alasdi S.N., Al-Sabur R., In-depth thermal analysis of different PIN configurations in friction stir spot welding of similar and dissimilar alloys, Journal of Manufacturing and Materials Processing, 9(6): 184, 2025, https://doi.org/10.3390/jmmp9060184

  8. Salgado-Lopez J.M., Ojeda-Elizarraras J.L., Silva-Hernandez A., Tello-Rico J.M., Failure of an autotank repaired by welding, Journal of Scientific and Technical Applications, 6(17): 1–9, http://doi.org/10.35429/jsta.2020.17.6.1.9

  9. Hamade R.F., Baydoun A.M.R., Nondestructive detection of defects in friction stir welded lap joints using computed tomography, Materials & Design, 162: 10–23, 2019, https://doi.org/10.1016/j.matdes.2018.11.034

  10. Abdelkader R., Ramou N., Khorchef M., Chetih N., Boutiche Y., Segmentation of x-ray image for welding defects detection using an improved Chan-Vese model, Materials Today Proceedings, 42(5): 2963–2967, 2021, https://doi.org/10.1016/j.matpr.2020.12.806

  11. Liao T.W., Classification of weld flaws with imbalanced class data, Expert Systems with Applications, 35(3): 1041–52, 2008, https://doi.org/10.1016/j.eswa.2007.08.044

  12. Chen Y., He Y., Wu L., Detection of welding defects using the YOLOV8-ELA algorithm, Applied Sciences, 15(9): 5204, 2025, https://doi.org/10.3390/app15095204

  13. Orlando M., De Maddis M., Razza V., Lunetto V., Non-destructive detection and analysis of weld defects in dissimilar pulsed GMAW and FSW joints of aluminium castings and plates through 3D X-ray computed tomography, The International Journal of Advanced Manufacturing Technology, 132(5–6): 2957–70, 2024, https://doi.org/10.1007/s00170-024-13576-x

  14. Liao T.W., Ni J., An automated radiographic NDT system for weld inspection: Part I – Weld extraction, NDT & E International, 29(3): 157–62, 1996, https://doi.org/10.1016/0963-8695(96)00009-6

  15. Da Silva R.R., Caloba L.P., Siqueira M.H.S., Rebello J.M.A., Pattern recognition of weld defects detected by radiographic test, NDT & E International, 37(6): 461–70, 2004, https://doi.org/10.1016/j.ndteint.2003.12.004

  16. Tou J.Y., Tay Y.H., Lau P.Y., Gabor filters and grey-level co-occurrence matrices in texture classification, [in:] MMU International Symposium on Information and Communications Technologies, pp. 197–202, 2007.

  17. Valentin P., Kounalakis T., Nalpantidis L., Weld classification using gray level cooccurrence matrix and local binary patterns, [in:] 2018 IEEE International Conference on Imaging Systems and Techniques, pp. 1–6, 2018, https://doi.org/10.1109/ist.2018.8577092

  18. Abidin Z., Anompa M.A., Muhtadan, Development of welding defects identifier application on radiographic film using gray level co-occurrence matrix and backpropagation, [in:] AIP Conference Proceedings, 1555: 70–74, 2013, https://doi.org/10.1063/1.4820996

  19. Ramana E.V., Penekalapati S.V., Namala K.K., Identification of weld sub-surface defects by radiographic images using texture features, E3S Web of Conferences, 552: 01017, 2024, https://doi.org/10.1051/e3sconf/202455201017

  20. Mery D., Berti M.A., Automatic detection of welding defects using texture features, Insight – Non-Destructive Testing and Condition Monitoring, 45(10): 676–81, 2003, https://doi.org/10.1784/insi.45.10.676.52952

  21. Wang X., Wong B.S., Tan C.S., Recognition of welding defects in radiographic images by using support vector machine classifier, Research Journal of Applied Sciences, Engineering and Technology, 2(3): 295-301, 2010.

  22. Hassan J., Awan A.M., Jalil A., Welding defect detection and classification using geometric features, [in:] 2012 10th International Conference on Frontiers of Information Technology, pp. 139–44, 2012, https://doi.org/10.1109/fit.2012.33

  23. Valavanis I., Kosmopoulos D., Multiclass defect detection and classification in weld radiographic images using geometric and texture features, Expert Systems With Applications, 7(12): 7606–14, 2010, https://doi.org/10.1016/j.eswa.2010.04.082

  24. Palma-Ramirez D., Ross-Veitia B.D., Font-Ariosa P., Espinel-Hernandez A., Sanchez-Roca A., Carvajal-Fals H., Nunez-Alvarez J.R., Hernandez-Herrera H., Deep convolutional neural network for weld defect classification in radiographic images, Heliyon, 10(9): e30590, 2024, https://doi.org/10.1016/j.heliyon.2024.e30590

  25. Zhang W., Liu W., Yu X., Kang D., Xiong Z., Lv X., Huang S., Li Y., Deep learning-based automated detection of welding defects in pressure pipeline radiograph, Coatings, 15(7): 808, 2025, https://doi.org/10.3390/coatings15070808

  26. Lone A.H., Siddiqui A.N., Noise models in digital image processing, Global Sci-Tech, 10(2): 63, 2018, https://doi.org/10.5958/2455-7110.2018.00010.1

  27. Singh H., Kaur S., Sharma P., Utilizing various filtering methodologies in digital image processing, [in:] 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), pp. 1–6, 2024, https://doi.org/10.1109/iceect61758.2024.10738905

  28. Al-Taie R., A review paper: digital image filtering processing, Technium, 3(9): 1–11, 2021, https://doi.org/10.47577/technium.v3i9

  29. Rivera-Aguilar B.A., Cuevas E., Luque-Chang A., Lopez J., Perez-Cisneros M., Pixel Interaction model for contrast enhancement: bridging social science and image processing, Applied Sciences, 14(23): 10887, 2024, https://doi.org/10.3390/app142310887

  30. Patel P., Bhandari A., A review on image contrast enhancement techniques, Smart Moves Journal Ijoscience, 5(7): 18–22, 2019, https://scispace.com/pdf/a-review-on-image-contrast-enhancement-techniques-mn53j9lvgm.pdf

  31. Lasnel R., Froute L., Kovscek A.R., Jolivet I.C., Creux P., Image processing and segmentation open source codes applied to FIB-SEM images of ultra-tight gas shales samples: enhanced pore space representativeness and mineral identification, Gas Science and Engineering, 138: 205610, 2025, https://doi.org/10.1016/j.jgsce.2025.205610

  32. Mageswari S.U., Sridevi M., Mala C., An experimental study and analysis of different image segmentation techniques, Procedia Engineering, 64: 36–45, 2013, https://doi.org/10.1016/j.proeng.2013.09.074

  33. Atta M.A., Imtiaz M., Hassan A., Saqib S., Image segmentation by using threshold techniques, Lahore Garrison University Research Journal of Computer Science and Information Technology, 2(2): 1–6, 2018, https://lgurjcsit.lgu.edu.pk/index.php/lgurjcsit/issue/view/29/18

  34. Chen X., Liu C., Xie D., Miao D., Image thresholding segmentation method based on adaptive granulation and reciprocal rough entropy, Information Sciences, 695: 121737, 2025, https://doi.org/10.1016/j.ins.2024.121737

  35. Haralick R.M., Shanmugam K., Dinstein I., Textural features for image classification, IEEE Transactions on Systems Man and Cybernetics, SMC-3(6): 610–621, 1973, https://doi.org/10.1109/tsmc.1973.4309314

  36. Utaminingrum F., Alqadri A.M., Somawirata I.K., Karim C., Septiarini A., Lin C.Y., Shih T.K., Feature selection of gray-level cooccurrence matrix using genetic algorithm with extreme learning machine classification for early detection of pole roads, Results in Engineering, 20: 101437, 2023, https://doi.org/10.1016/j.rineng.2023.101437">https://doi.org/10.1016/j.rineng.2023.101437.

  37. Iqbal N., Mumtaz R., Shafi U., Zaidi S.M.H., Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms, PeerJ Computer Science, 7: 1–26, 2021, https://doi.org/10.7717/peerj-cs.536

  38. Jobanputra R., Clausi D.A., Preserving boundaries for image texture segmentation using grey level co-occurring probabilities, Pattern Recognition, 39(2): 234–245, 2006, https://doi.org/10.1016/j.patcog.2005.07.010

  39. Kwak J.T., Xu S., Wood B.J., Efficient data mining for local binary pattern in texture image analysis, Expert Systems With Applications, 42(9): 4529–4539, 2015, https://doi.org/10.1016/j.eswa.2015.01.055

  40. Lan S., Liao X., Fan H., Hu S., Pan Z., A multi-channel framework based Local Binary Pattern with two novel local feature descriptors for texture classification, Digital Signal Processing, 140: 104124, 2023, https://doi.org/10.1016/j.dsp.2023.104124

  41. Kaur N., Nazir N., Manik, A review of local binary pattern based texture feature extraction, 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2021, https://doi.org/10.1109/ICRITO51393.2021.9596485

  42. Ojala T., Pietikainen M., Harwood D., A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, 29(1): 51–59, 1996, https://doi.org/10.1016/0031-3203(95)00067-4

  43. Ojala T., Pietikainen M., Maenpaa T., Gray scale and rotation invariant texture classification with local binary patterns, [in:] Computer Vision – ECCV 2000, 1842: 404–420, 2000, https://doi.org/10.1007/3-540-45054-8 27.

  44. Ikram K., Djilali K., Abdennasser D., Al-Sabur R., Ahmed B., Sharkawy A.N., Comparative analysis of fouling resistance prediction in shell and tube heat exchangers using advanced machine learning techniques, Research on Engineering Structures and Materials, 10(1): 253–270, 2024, https://doi.org/10.17515/resm2023.858en0816

  45. Shi B., Liu J., Nonlinear metric learning for kNN and SVMs through geometric transformations, Neurocomputing, 318: 18–29, 2018, https://doi.org/10.1016/j.neucom.2018.07.074

  46. Fan Z., Xie J.K., Wang Z.Y., Liu P.C., Qu S.J., Huo L., Image classification method based on improved KNN algorithm, Journal of Physics Conference Series, 1930(1): 012009, 2021, https://doi.org/10.1088/1742-6596/1930/1/012009

  47. Allwein E.L., Schapire R.E., Singer Y., Reducing multiclass to binary: a unifying approach for margin classifiers, Journal of Machine Learning Research, 1: 113–141, 2000, https://www.jmlr.org/papers/volume1/allwein00a/allwein00a.pdf

  48. Liu Y., Bi J.W., Fan Z.P., A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm, Information Sciences, 394–395: 38–52, 2017, https://doi.org/10.1016/j.ins.2017.02.016

  49. Bahedh A.S., Mishra A., Al-Sabur R., Jassim A.K., Machine learning algorithms for prediction of penetration depth and geometrical analysis of weld in friction stir spot welding process, Metallurgical Research & Technology, 119(3): 305, 2022, https://doi.org/10.1051/metal/2022032

  50. Al-Sabur R., Mishra A., Khalaf H.I., Mastering friction stir welding (FSW) with machine learning (ML): a comprehensive guide to algorithms and applications, [in:] Using Computational Intelligence for Sustainable Manufacturing of Advanced Materials, pp. 387–416, 2025, https://doi.org/10.4018/979-8-3693-7974-5.ch017

  51. Encord, Confusion Matrix, Encord Computer Vision Glossary, https://encord.com/glossary/confusion-matrix/ (access: 2.09.2025).

  52. Sokolova M., Lapalme G., A systematic analysis of performance measures for classification tasks, Information Processing & Management, 45(4): 427–37, 2009, https://doi.org/10.1016/j.ipm.2009.03.002

  53. Mahmoud K.H., Abdel-Jaber G.T., Sharkawy A.N., Neural network-based classifier for collision classification and identification for a 3-DOF industrial robot, Automation, 5(1): 13–34, 2024, https://doi.org/10.3390/automation5010002