Superpixel Image Segmentation-Based Particle Size Distribution Analysis of Fragmented Rock

Abstract

Research on the particle size of blast piles has always been an essential issue in mining engineering. Reasonable blasting parameters can reduce mining costs and reduce the workload of secondary crushing, which can significantly improve mining efficiency. The usual particle size analysis methods include the sieving method, the large particle size statistical method and other manual measurement methods. Nevertheless, these methods have the disadvantages of a high labor intensity, low efficiency and low precision. This paper analyzes UAV image information based on the single-picture photogrammetry method of computer image processing technology. A two-dimensional empirical wavelet transform (EWT) is used for image noise reduction. The nonlocal multiscale fractional differential (NMFD) enhances the texture of dark images and uses superpixel image segmentation technology so that the processed image can meet the granularity statistical study requirements of blast piles. The research results show that the accuracy of the ore particle size distribution by the method proposed in this paper is more than 90%.

Keywords

• Digital image processing
• granularity distribution
• image denoising
• image enhancement
• superpixel generated image