An Automated Deep Segmentation and Spatial-Statistics Approach for Post-Blast Rock Fragmentation Assessment

--We introduce an end-to-end pipeline that leverages a fine-tuned YOLO12l-seg model--trained on over 500 annotated post-blast images--to deliver real-time instance segmentation (Box mAP@0.5 0.769, Mask mAP@0.5 0.800 at 15 FPS). High-fidelity masks are converted into normalized 3D coordinates, from which we extract multi-metric spatial descriptors: principal component directions, kernel density hotspots, size-depth regression, and Delaunay edge statistics. We present four representative examples to illustrate key fragmentation patterns. Experimental results confirm the framework's accuracy, robustness to small-object crowding, and feasibility for rapid, automated blast-effect assessment in field conditions. Accurate assessment of rock fragmentation following blasting is critical for optimizing downstream mining efficiency and safety [1].