An enhanced segmentation method for 3D point cloud of tunnel support system using PointNet++ and coverage-voted strategy algorithms

3D laser scanning technology is widely used in underground openings for high-precision, rapid, and non-destructive structural evaluations. Segmenting large 3D point cloud datasets, particularly in coal mine roadways with multi-scale targets, remains challenging. This paper proposes an enhanced segmentation method integrating improved PointNet++ with a coverage-voted strategy. The coverage-voted strategy reduces data while preserving multi-scale target topology. The segmentation is achieved using an enhanced PointNet++ algorithm with a normalization preprocessing head, resulting in a 94% accuracy for common supporting components. Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20% and 2%, respectively, and improve Intersection over Union (IoU) for bearing plate segmentation by 58% and 20%. The accuracy of the current pretraining segmentation model may be affected by variations in surface support components, but it can be readily enhanced through re-optimization with additional labeled point cloud data. This proposed method, combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate, provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.