3D rock fragmentation analysis using lidar, based on point cloud deep learning segmentation and synthetic data

Accurate online measurement of particle size distribution is crucial in mining, tunnelling , and mineral processing industries to enable intelligent process control and optimization, ultimately enhancing efficiency and productivity. The current method for rock fragmentation relies on 2D image analysis, which is highly dependent on optimal lighting conditions, limiting its applicability and robustness in the challenging lighting environments commonly found in mining. This study diverges from the prevalent 2D image and photogrammetry approaches in rock fragmentation analysis, and pioneers a novel approach by harnessing laser scanner data for point cloud segmentation, offering a promising solution to overcome the limitations of image analysis techniques. By leveraging laser scanner data, a robust framework for rock fragmentation analysis is developed that is tailored to the specific challenges related to lighting situations. To avoid the laborious task of collecting and labelling point cloud datasets, this research introduces an innovative approach of using synthetic labeled datasets of scanned rockpiles. A platform is developed to automatically create and scan labeled point clouds of rock piles, facilitating the utilization of transfer learning . The synthetic 3D dataset was used to train a deep learning model for precise segmentation of rock instances in three-dimensional coordinates, providing an accurate representation of the rock object in 3D. The accuracy of the developed predictive model was tested and validated on experimental laser scanning data of three different rock piles. The proposed method depends on coordinate data instead of RGB information, rendering it particularly applicable in challenging conditions such as underground mining, night shifts, or situations where maintaining optimal lighting conditions is difficult or costly. The findings present a significant leap forward in rock fragmentation analysis, opening avenues for enhanced practices in diverse mining environments. • Developed a deep learning approach for 3D rock fragmentation using LiDAR point cloud data. • Designed an adjustable platform to generate labeled synthetic datasets for point clouds. • Proposed a model for suboptimal lighting conditions such as underground mining and night shifts. • Validated model accuracy with experimental data of a 3D scanner for size distribution. • Examined point cloud density and rock size thresholds for robust 3D segmentation.