Abstract
Though copper products are important raw materials in industrial production, there is little domestic research focused on copper strip surface defects inspection based on automated visual inspection. According to the defect image characteristics on copper strips surface, a defect detection algorithm is proposed on the basis of wavelet-based multivariate statistical approach. First, the image is divided into several sub-images, and then each sub-image is further decomposed into multiple wavelet processing units. Then each wavelet processing unit is decomposed by 1-D db4 wavelet function. Then multivariate statistics of Hotelling T2 are applied to detect the defects and SVM is used as defect classifier. Finally, the defect detection performance of the proposed approach is compared with traditional method based on grayscale. Experimental results show that the proposed method has better performance on identification, especially its application in the ripple defects can achieve 96.7% accuracy, which was poor in common algorithms.
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References
Cass, R., DePietro, J.: Computational Intelligence Methods for Process Discovery. Engineering Applications of Artificial Intelligence 11, 119–138 (1998)
Bu, H.G., Wang, J., Huang, X.B.: Fabric Defect Detection based on Multiple Fractal Features and Support Vector Data Description. Engineering Applications of Artificial Intelligence 22, 224–235 (2009)
Chen, R.C., Hsieh, C.H.: Web Page Classification based on a Support Vector Machine using a Weighted Vote Schema. Expert Systems with Applications 31, 427–435 (2006)
Bharati, M.H., Liu, J.J., MacGregor, J.F.: Image Texture Analysis: Methods and Comparisons. Chemometrics and Intelligent Laboratory Systems 72, 57–71 (2004)
Zhang, Y.J.: Content-based Visual Information Search. Science Press, Beijing (2003)
Stojanovic, R., Mitropulos, P., Koulamas, C., Karayiannis, Y., Koubias, S., Papadopoulos, G.: Real-Time Vision-Based System for Textile Fabric Inspection. Real-Time Imaging 7, 507–518 (2001)
Brzakovic, D., Beck, H., Sufi, N.: An Approach to Defect Detection in Materials Characterized by Complex Textures. Pattern Recognition 23(1/2), 99–107 (1990)
Li, T.S.: Applying Wavelets Transform, Rough Set Theory and Support Vector Machine for Copper Clad Laminate Defects Classification. Expert systems with Applications 36(3), 5822–5829 (2008)
Latif-Amet, A., Ertuzun, A., Ercil, A.: An Efficient Method for Texture Defect Detection: Sub-band Domain Co-occurrence Matrices. Image and Vision Computing 28, 763–774 (2002)
Su, J., Li, M.: Research of Metal Fracture Image Classification based on GLCM. Computer Engineering and Applications 44(9), 223–225 (2008)
Zhong, K.H., Ding, M.Y., Zhou, C.P.: Texture Defect Inspection Method using Difference Statistics Feature in Wavelet Domain. System Engineering and Electronics 26(5), 660–665 (2004)
Pichler, O., Teuner, A., Hosticka, B.J.: A Comparison of Texture Feature Extraction using Adaptive Gabor Filtering, Pyramidal and Tree Structured Wavelet Transforms. Pattern Recognition 29, 733–742 (1996)
Arivazhagan, S., Ganesan, L.: Texture Segmentation using Wavelet Transform. Pattern Recognition 24, 3197–3203 (2003)
Kim, S.C., Kang, T.J.: Texture Classification and Segmentation using Wavelet Packet Frame and Gaussian Mixture Model. Pattern Recognition 40, 1207–1221 (2007)
Chen, T., Kuo, C.C.J.: Texture Analysis and Classification with Tree-structured Wavelet Transform. IEEE Transactions on Image Processing 2(4), 429–441 (1993)
Liu, H., Mo, Y.L.: 2D Wavelet Transform with Different Adaptive Wavelet Based for Regular Texture Defect Inspection. Journal of Image and Graphics 4(4), 349–352 (1999)
Tsai, D.M., Hsiao, B.: Automatic Surface Inspection Using Wavelet Reconstruction. Pattern Recognition 34, 1285–1305 (2001)
Lin, H.D.: Computer-aided Visual Inspection of Surface Defects in Ceramic Capacitor Chips. Materials Processing Technology 189, 19–25 (2007)
Xu, C.F., Li, G.K.: Practical Wavelet Method. Huazhong University of Science and Technology Press, Wuhan (2001)
Hotelling, H.: Multivariate Quality Control. Techniques of Statistical Analysis. In: Eisenhart, Hastasy, Wallis (eds.) McGraw-Hill, New York (1947)
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Zhang, XW., Xu, LZ., Ding, YQ., Fan, XN., Gu, LP., Sun, H. (2010). Inspection of Surface Defects in Copper Strip Based on Machine Vision. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15621-2_34
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DOI: https://doi.org/10.1007/978-3-642-15621-2_34
Publisher Name: Springer, Berlin, Heidelberg
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