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.
Highlights
At present, machine vision–based surface inspection technology has been widely used in the detection and identification of surface defects of various industrial products due to its non-contact and real-time detection properties [1]
The novelty of our work lies in introducing discrete nonseparable shearlet transform (DNST) into the surface defect recognition of continuous casting slabs with the complex backgrounds, fusing gray-level co-occurrence matrix (GLCM) texture features, and using a suitable
Frequency tiles of a discrete nonseparable shearlet transform (DNST) filter corresponding to the first scale. (c) The frequency tiles of a DNST filter corresponding to the second scale
Summary
Machine vision–based surface inspection technology has been widely used in the detection and identification of surface defects of various industrial products due to its non-contact and real-time detection properties [1]. Of the above mentioned methods, the wavelet-based feature extraction (for example, references [1,2,8,10,11]) is the more effective and more studied technology These methods have achieved some results, the recognition accuracy of surface defects of continuous casting slabs needs to be further improved with the increasingly strict quality requirements of users. The proposed feature extraction approach is named discrete nonseparable shearlet transform gray-level co-occurrence matrix kernel spectral regression (DNST-GLCM-KSR), which combines multi-scale and multi-directional features of DNST with texture features of GLCM and uses KSR to remove redundant features. The novelty of our work lies in introducing DNST into the surface defect recognition of continuous casting slabs with the complex backgrounds, fusing GLCM texture features, and using a suitable. 5 describes the experimental results and discussions, by conclusions in 6
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