Pellet image segmentation model of superpixel feature-based support vector machine in digital twin

Abstract

A digital twin model based on superpixel features is established to solve the problem of noise and similar gray values between foreground and background of pellet images. With superpixel as the basic unit of segmentation, the influence of single pixel on segmentation results is reduced, and allows for higher segmentation accuracy. The gray-level co-occurrence matrix is used to represent the superpixel characteristic information, and the color moment and gray level distribution are combined to comprehensively characterize the superpixel. Through principal component analysis and correlation analysis, The feature compression of superpixel is realized, and the computational efficiency is improved. The superpixel binary classification data set is built, and the multi-dimensional feature information of superpixel is extracted as input vector to train the binary classification model of SVM, and the image segmentation problem is transformed into foreground and background classification problem. A multi-scale superpixel segmentation boundary optimization method is proposed to further refine the boundary region of foreground and background. A four-neighborhood search algorithm is proposed to reduce the missegmentation rate of edge superpixels. Experimental results show that the accuracy of the proposed method can reach 95.87%, the precision of image edge segmentation is high, and the foreground and background of granular image are accurately segmented. The digital twin model is established which can provide basis for the subsequent visual analysis and decision.