Application of multi-scale feature extraction to surface defect classification of hot-rolled steels

作者：Xu, Ke^*; Ai, Yong-hao; Wu, Xiu-yong

来源：International Journal of Minerals,Metallurgy and Materials, 2013, 20(1): 37-41.

DOI：10.1007/s12613-013-0690-y

• hot rolling
• strip metal
• surface defects
• classification
• feature extraction

Feature extraction is essential to the classification of surface defect images. The defects of hot-rolled steels distribute in different directions. Therefore, the methods of multi-scale geometric analysis (MGA) were employed to decompose the image into several directional subbands at several scales. Then, the statistical features of each subband were calculated to produce a high-dimensional feature vector, which was reduced to a lower-dimensional vector by graph embedding algorithms. Finally, support vector machine (SVM) was used for defect classification. The multi-scale feature extraction method was implemented via curvelet transform and kernel locality preserving projections (KLPP). Experiment results show that the proposed method is effective for classifying the surface defects of hot-rolled steels and the total classification rate is up to 97.33%.

• 出版日期2013-1
• 单位北京科技大学