Point-cloud segmentation and volume estimation of dense medium piles based on an improved PointNet++

This study proposes a deep learning method based on an improved PointNet++ network for point-cloud segmentation and volume estimation of dense medium piles in coal preparation plants. By optimizing the original PointNet++ model, two main improvements are introduced: an enhanced ball neighborhood feature extraction algorithm and a simplified network architecture. These improvements significantly enhance segmentation accuracy under non-uniform point density and complex local morphologies, while also increasing computational efficiency. Experimental results demonstrate that, compared with traditional methods such as plane clipping, RANSAC plane fitting, and progressive morphological filtering, the improved model achieves higher segmentation accuracy, particularly at pile boundaries and slope regions. The mean mIoU increases from 88.94% to 90.27%, with improvements mainly observed in critical boundary regions and areas affected by foreign objects, thereby reducing volume calculation errors and improving system reliability. In addition, the processing time is reduced by 50%. For volume estimation, a slicing method based on the segmented point cloud is used. The results show that, with a slice thickness of 1.375 mm, the volume calculation error is minimized, and in practical applications, the volume estimation error is consistently kept below 10%. This study provides a theoretical foundation and practical approach for medium replenishment and intelligent grasping systems in coal preparation plants, with broad industrial application prospects.