Ore segmentation based on laser point clouds

The granularity of ore particles is crucial for the efficiency and sustainability of steel smelting operations. This study introduces an innovative segmentation technique for ore particles utilizing a depth camera to capture point cloud data on conveyor belts, which is then processed into a two-dimensional image for analysis. By employing a novel dual-encoding neural network structure, Swin-FUNet, for semantic segmentation, followed by morphological operations and concave point segmentation, the method significantly enhances the accuracy of particle segmentation. Comparative experiments confirm the effectiveness of this approach, offering potential improvements in material utilization, smelting efficiency, and equipment longevity for the steel industry.