**Xiaoming Liu 1, Ke Xu 2,\*, Peng Zhou <sup>1</sup> and Huajie Liu <sup>2</sup>**


Received: 17 September 2019; Accepted: 25 October 2019; Published: 1 November 2019

**Featured Application: First, the surface defect inspection characteristics of continuous casting slab is that the slab moves slowly on the production line, so the feature extraction method does not need too fast a calculation speed. Second, the inspection di**ffi**culties of continuous casting slabs are the defect with complex backgrounds, so some common feature extraction methods cannot meet these needs. DNST (discrete non-separable shearlet transform) is a new multiresolution analysis method with moderate computing speed. It can extract images information from multiple scales and directions. Therefore, this paper proposed a DNST-GLCM-KSR (discrete non-separable shearlet transform-gray-level co-occurrence matrix-kernel spectral regression) feature extraction method. The method is suitable for surface defects inspection with complex background and moderate running speed of production line.**

**Abstract:** A new feature extraction technique called DNST-GLCM-KSR (discrete non-separable shearlet transform-gray-level co-occurrence matrix-kernel spectral regression) is presented according to the direction and texture information of surface defects of continuous casting slabs with complex backgrounds. The discrete non-separable shearlet transform (DNST) is a new multi-scale geometric analysis method that provides excellent localization properties and directional selectivity. The gray-level co-occurrence matrix (GLCM) is a texture feature extraction technology. We combine DNST features with GLCM features to characterize defects of the continuous casting slabs. Since the combination feature is high-dimensional and redundant, kernel spectral regression (KSR) algorithm was used to remove redundancy. The low-dimension features obtained and labels data were inputted to a support vector machine (SVM) for classification. The samples collected from the continuous casting slab industrial production line—including cracks, scales, lighting variation, and slag marks—and the proposed scheme were tested. The test results show that the scheme can improve the classification accuracy to 96.37%, which provides a new approach for surface defect recognition of continuous casting slabs.

**Keywords:** continuous casting slabs; surface defect classification; discrete non-separable shearlet transform; gray-level co-occurrence matrix; kernel spectral regression
