Abstract
This paper describes the process of detection of defects in metallic pieces through the analysis of X-ray images. The images used in this work are highly variable (several different pieces, different views, variability introduced by the inspection process such as positioning the piece). Because of this variability, the sliding window technique has been used, an approach based on data mining. Experiments have been carried out with various window sizes, several feature selection algorithms and different classification algorithms, with a special focus on learning unbalanced data sets. The results show that Bagging achieved significantly better results than decision trees by themselves or combined with SMOTE or Undersampling.
This work was supported by Project Magno MAGNO2008-1028-CENIT. We also thanks to Grupo Antolin for providing us the X-ray images used in the experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cartz, L.: Nondestructive Testing: Radiography, Ultrasonics, Liquid Penetrant, Magnetic Particle, Eddy Current. Asm International (1995)
Spencer, F.: Visual inspection research project report on benchmark inspections. Technical report, Office of Aviation Research Washington, D.C. 20591: U.S. Department of Transportation, Federal Aviation Administration,Washington, DC (1996)
Liao, T.: Classification of welding flaw types with fuzzy expert systems. Expert Systems with Applications 25, 101–111 (2003)
Rebuffel, V., Sood, S., Blakeley, B.: Defect detection method in digital radiography for porosity in magnesium casting. In: Materials Evaluation, ECNDT 2006 (2006)
Hanke, R., Hassler, U., Heil, K.: Fast automatic x-ray image processing by means of a new multistage filter for background modelling. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 1994, vol. 1, pp. 392–396. IEEE (1994)
Ng, H.: Automatic thresholding for defect detection. Pattern Recognition Letters 27, 1644–1649 (2006)
Saravanan, T., Bagavathiappan, S., Philip, J., Jayakumar, T., Raj, B.: Segmentation of defects from radiography images by the histogram concavity threshold method. Insight-Non-Destructive Testing and Condition Monitoring 49, 578–584 (2007)
Anand, R., Kumar, P., et al.: Flaw detection in radiographic weld images using morphological approach. NDT & E International 39, 29–33 (2006)
Wang, M., Chai, L.: Application of an improved watershed algorithm in welding image segmentation. Transactions China Welding Institution 28, 13 (2007)
Anand, R., Kumar, P., et al.: Flaw detection in radiographic weldment images using morphological watershed segmentation technique. NDT & E International 42, 2–8 (2009)
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
García-Osorio, C., Díez-Pastor, J.F., Rodríguez, J.J., Maudes, J.: License plate number recognition - new heuristics and a comparative study of classifiers. In: Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics, Robotics and Automation, ICINCO 2008, vol. 1, pp. 268–273 (2008)
Jones, M., Rehg, J.: Statistical color models with application to skin detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, IEEE (1999)
Dlagnekov, L.: License plate detection using adaboost. Computer Science and Engineering Department, San Diego (2004)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. 511–518. IEEE (2001)
Wang, Y., Sun, Y., Lv, P., Wang, H.: Detection of line weld defects based on multiple thresholds and support vector machine. NDT & E International 41, 517–524 (2008)
Mery, D.: Automated detection of welding discontinuities without segmentation. Materials Evaluation, 657–663 (2011)
Belaifa, S., Tridi, M., Nacereddine, N.: Weld defect classification using em algorithm for gaussian mixture model. In: SETIT Tunisia 2005 (2005)
Montabone, S., Soto, A.: Human detection using a mobile platform and novel features derived from a visual saliency mechanism. Image and Vision Computing 28, 391–402 (2010)
Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 610–621 (1973)
Hall, M.: Correlation-based feature selection for machine learning. PhD thesis, The University of Waikato (1999)
Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Machine Learning-International Workshop Then Conference, vol. 20, pp. 856–863 (2003)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2002)
Chawla, N., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter 6, 1–6 (2004)
Fawcett, T.: An introduction to roc analysis. Pattern Recognition Letters 27, 861–874 (2006)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)
Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kauffman (1993)
Cieslak, D.A., Hoens, T.R., Chawla, N.V., Kegelmeyer, W.P.: Hellinger distance decision trees are robust and skew-insensitive. Data Min. Knowl. Discov. 24, 136–158 (2012)
Provost, F., Domingos, P.: Tree induction for probability-based ranking. Machine Learning 52, 199–215 (2003)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Dietterich, T.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 10, 1895–1923 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Díez-Pastor, J.F., García-Osorio, C., Barbero-García, V., Blanco- Álamo, A. (2013). Imbalanced Learning Ensembles for Defect Detection in X-Ray Images. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_68
Download citation
DOI: https://doi.org/10.1007/978-3-642-38577-3_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38576-6
Online ISBN: 978-3-642-38577-3
eBook Packages: Computer ScienceComputer Science (R0)