A Physics-Based Approach to Rock Bolt Detection and Spatial Monitoring

Rock bolts are the primary ground support mechanism in underground mining. Yet verification of their installation is rarely captured in a spatially precise, retrievable form, leaving operators without an auditable as-built record for regulatory review or post-incident reconstruction. This paper presents an automated rock bolt detection process that closes this documentation gap using dense point clouds from an underground hard rock mine acquired by terrestrial laser scan. The method computes per-point ambient occlusion (AO) on closure plane-sealed chambers using a PCV implementation of the ShadeVIS principle, forms candidates from a multi-scale protrusion field, and segments them by prominence watershed before classifying each candidate with PCA-based geometric descriptors, without machine learning or training data. Installation perpendicularity is applied as a per-detection confidence cue, and detections are reported in confidence tiers that concentrate human review on the ambiguous minority. Validated against a database of 1447 bolts across 20 walls in two areas of an underground mine, the system achieved an overall recall of 83.7%, with human review completing the inventory to 100%. The physics-based design transfers across bolt types and mine geometries through parameter re-tuning rather than retraining, addressing the core limitation of deep learning methods, which require site-specific labelled datasets.