Improved Defect Classification of Printed Circuit Board Using SVM
This paper proposes an analytical approach to make correct recognition between the true defect and the pseudo defect with visual inspection of the electronic board. Some classification approaches have already been proposed for the limited kinds of defects but there have been incorrect recognitions for the defects which is difficult to handle with the visual inspection. This paper proposes the approach to reduce the incorrect recognition for the defects using Support Vector Machine. Real electronic board image data are tested and evaluated with the proposed approach. It is shown that the proposed approach gives efficient classification with some new features with a proper analysis on histogram of image according to the proposed evaluation criteria for the performance.
KeywordsSupport Vector Machine Defect Image Average Gray Level True Defect Incorrect Recognition
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- 1.Roh, B., et al.: A Neural Network Approach to Defect Classification on Printed Circuit Boards. Journal of the Japan Society of Precision Engineering 67(10), 1621–1626 (2001) (in Japanese)Google Scholar
- 2.Tanaka, T., Hotta, S., Iga, T., Nakamura, T.: Automatic Image Filter Creation System: To Use for a Defect Classification System. IEICE Technical Report 106(448), 195–198 (2007)Google Scholar
- 4.Kondo, K., et al.: Defect Classification Using Random Feature Selection and Bagging. The Journal of the Institute of Image Electronics Engineers of Japan 38(1), 9–15 (2009) (in Japanese)Google Scholar
- 5.Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Inc. (1998)Google Scholar
- 6.Iwahori, Y., Futamura, K., Adachi, Y.: Discrimination of True Defect and Indefinite Defect with Visual Inspection Using SVM. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part IV. LNCS, vol. 6884, pp. 117–125. Springer, Heidelberg (2011)CrossRefGoogle Scholar