Automatic Extraction of Accurate Particle Sizes from a 3D Point Cloud of Rock Masses Based on a Hybrid Modified Bounding Box Algorithm - Mining, Metallurgy & Exploration (2024)

Particle size distribution (PSD) is an essential parameter in assessing the overall efficiency of blasting operations in mines and the subsequent mine-to-mill process in the mining industry. Despite some drawbacks of 2D image analysis techniques in accurately estimating particle sizes in PSD, the mining industry has relied on them for the last three decades. This study proposes the 3D rock fragmentation measurement (3DFM) technique for deducting the accurate dimensions of 3D rock particles for the PSD. 3DFM has utilized different processing algorithms. Images of different views of the non-touching rock particles of varying sizes have been acquired as a data acquisition step of structure from motion technology for generating sparse point cloud. Dense point cloud reconstruction is used to avail finer details of the point cloud using clustering views for the multi-view stereo algorithm. Random sample consensus (RANSAC) algorithm coupled with an unsupervised classification using the density-based spatial clustering of applications with noise (DBSCAN) classifier is employed to extract the rock clusters from the 3D point cloud. Finally, the accurate rock sizes are derived using the hybrid bounding box rotation identification (HYBBRID) algorithm with a root mean square error (RMSE) of 0.10 cm for length, 0.10 cm for breadth, and 0.32 cm for depth. The PSD of rock fragments obtained from the proposed 3DFM technique is found to be matching with the results of mechanical sieving and manual gauging with an R2 values of 0.98 and 0.99, respectively. The 3DFM method can be considered cheaper, more accurate, and computationally faster in determining the rock dimensions for the PSD determination method to enhance the productivity of the mining industry.