{"ID":2889854,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20126","arxiv_id":"2507.20126","title":"An Automated Deep Segmentation and Spatial-Statistics Approach for Post-Blast Rock Fragmentation Assessment","abstract":"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.","short_abstract":"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 extr...","url_abs":"https://arxiv.org/abs/2507.20126","url_pdf":"https://arxiv.org/pdf/2507.20126v1","authors":"[\"Yukun Yang\"]","published":"2025-07-27T04:25:29Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"stat.ML\"]","methods":"[]","has_code":false}