Recognition of surface defects in continuous casting slabs based on Contourlet transform

A new recognition method of surface defects based to the characteristics of continuous casting slabs. Sample images were on Contourlet transform was proposed according decomposed into multiple subbands with different scales and directions by Contourlet transform. The Contourlet coefficients of subbands and the textural features of sample images were combined into a high-dimensional feature vector. Supervised kernel locality preserving projection (SKLPP) was applied to the high-dimensional feature vector for dimension reduction, which resulted in a low-dimensional feature vector. The resulted feature vector was inputted to a support vector machine (SVM) for recognition of surface defects. The method was tested with sample images from an industrial production line, including cracks, scales, non-uniform illumination, and slags. The test results show that the recognition rate of these sample images is 94.35%, which is better than that by Gabor wavelet.