Surface Defect Recognition for Metals Based on Feature Fusion of Shearlets and Wavelets

Abstract

Surface defects of metals are often with complexity and diversity. Wavelet transform is effective to detect singularities, it is applicable for recognition of point defects. As a relatively new method of multiscale geometric analysis(MGA), shearlet transform has good performance of directivity and optimal approximation, and it is applicable for recognition of defects with determined directions. A new recognition method of surface defects for metals is proposed, and it is based on feature fusion of shearlet transform and wavelet transform. Images of metals are decomposed into multiple directional sub-bands with shearlet transform and wavelet transform respectively. Then, means and variances of all sub-bands are computed and combined as a high dimensional feature vector. kernel locality preserving projection(KLPP) is applied to the high dimensional feature vector to remove redundant information between features. A low dimensional feature vector is generated and input to support vector machine(SVM) for classification of defects. Tested with samples captured from production lines of three typical metals, including high temperature slabs, medium and heavy plates and aluminium strips. Results show that classification rates of testing sets for high temperature slabs, medium and heavy plates and aluminium strips are 93.95%, 98.27% and 92.5% respectively. It is verified that the proposed method is a general algorithm of defect recognition for different types of metals.