Abstract
The automated flaw detection in aluminium castings consists of two steps: a) identification of potential defects using image processing techniques, and b) classification of potential defects into ‘defects’ and ‘regular structures’ (false alarms) using pattern recognition techniques. In the second step, since several features can be extracted from the potential defects, a feature selection must be performed. In addition, since the two classes have a skewed distribution, the classifier must be carefully trained. In this paper, we deal with the classifier design, i.e., which features can be selected, and how the two classes can be efficiently separated in a skewed class distribution. We propose the consideration of a self-organizing feature map (SOM) approach for stratified dimensionality reduction for simplified model building. After a feature selection and data compression stage, a neuro-fuzzy method named ANFIS is used for pattern classification. The proposed method was tested on real data acquired from 50 noisy radioscopic images, where 23000 potential defects (with only 60 real detects) were segmented and 405 features were extracted in each potential defect. Using the new method, a good classification performance was achieved using only two features, yielding an area under the ROC curve A z =0.9976.
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Mery, D., Jaeger, T., Filbert, D.: A review of methods for automated recognition of casting defects. Insight 44, 428–436 (2002)
Aoki, K., Suga, Y.: Application of artificial neural network to discrimination of defect type automatic radiographic testing of welds. ISI International 39, 1081–1087 (1999)
Mery, D., da Silva, R., Caloba, L., Rebello, J.: Pattern recognition in the automatic inspection of aluminium castings. Insight 45, 475–483 (2003)
Liao, T., Li, D., Li, Y.: Detection of welding flaws from radiographic images with fuzzy clustering methods. Fuzzy Sets and Systems 108, 145–158 (1999)
Liao, T.: Classification of welding flaw types with fuzzy expert systems. Fuzzy Sets and Systems 108, 145–158 (1999)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11, 586–600 (2000)
Jang, J.S.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23, 665–684 (1993)
Boerner, H., Strecker, H.: Automated X-ray inspection of aluminum casting. IEEE Trans. Pattern Analysis and Machine Intelligence 10, 79–91 (1988)
Mery, D., Filbert, D.: Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence. IEEE Trans. Robotics and Automation 18, 890–901 (2002)
Hall, M.: Correlation-Based Feature Selection for Machine Learning. Ph.D thesis, Waikato University, Department of Computer Science, NZ (1998)
Jang, J.S., Sun, C.: Neuro-fuzzy modeling and control. Proceedings of the IEEE 83, 378–406 (1995)
Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley & Sons, Inc., New York (2001)
Mery, D.: Crossing line profile: a new approach to detecting defects in aluminium castings. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 725–732. Springer, Heidelberg (2003)
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Hernández, S., Sáez, D., Mery, D. (2004). Neuro-Fuzzy Method for Automated Defect Detection in Aluminium Castings. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_100
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DOI: https://doi.org/10.1007/978-3-540-30126-4_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23240-7
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