Abstract: Large-scale point clouds generated during online inspection of aggregate particles at conveyor belt stations in shield tunneling muck recycling result in low segmentation efficiency, while downsampling often leads to the loss of morphological details. To address these issues, an online-oriented instance segmentation algorithm for particle point clouds (Effi-Particle-Seg) is developed, together with an approximate geometric feature estimation method. The proposed approach projects three-dimensional (3D) point clouds into binary images for two-dimensional (2D) instance segmentation while simultaneously establishing pixel-point correspondences during projection, enabling lossless backprojection of 2D instance labels to the original 3D points. This process generates per-particle instance point sets without downsampling, thereby preserving complete 3D information. For scenarios in which adjacent or lightly touching particles cause foreground adhesion during projection, a distance transform-based marker-controlled watershed algorithm is introduced to reduce the risk of undersegmentation. Based on the segmented instances, automated and efficient estimation of triaxial dimensions, volume, and shape indices (e.g., flakiness and sphericity) is achieved by combining oriented bounding boxes with air-profile recording. The results demonstrate that (1) for a point cloud containing 951 179 points, Effi-Particle-Seg achieves a segmentation time of 0.77 s, reducing runtime by 96% compared with traditional DBSCAN (21.3 s) while preserving full 3D details; (2) in foreground adhesion scenarios, the proposed marker-controlled watershed algorithm effectively suppresses undersegmentation and improves the reliability of separating neighboring particle instances; (3) for randomly selected samples, the absolute relative error between the measured volume and the point cloud-reconstructed volume is below 4%, demonstrating the accuracy and feasibility of the proposed geometric estimation method; and (4) on a standard PC, the system outputs particle-instance segmentation results and geometric parameters for a 40 cm×40 cm field of view in approximately 1 s, meeting the real-time requirements of conveyor belt inspection.

Key words: shield muck, aggregate utilization, three-dimensional scanning technology, particle point cloud segmentation, particle feature perception