一种用于爆后岩石破碎评估的自动化深度分割与空间统计方法
An Automated Deep Segmentation and Spatial-Statistics Approach for Post-Blast Rock Fragmentation Assessment
AI总结：
本文提出融合微调YOLO12l-seg实例分割与3D空间统计的端到端流程，用于实时、自动化评估爆后岩石破碎效果。
摘要：
我们提出一种端到端流程，该流程利用在500多张带标注爆后图像上训练的微调YOLO12l-seg模型，实现实时实例分割（Box mAP@0.5约0.769，Mask mAP@0.5约0.800，速度约15 FPS）。高保真掩膜被转换为归一化3D坐标，并从中提取多指标空间描述符：主成分方向、核密度热点、尺寸-深度回归以及Delaunay边统计。我们给出四个代表性示例以说明关键破碎模式。实验结果证实了该框架的准确性、对小目标拥挤情形的鲁棒性，以及在现场条件下快速、自动化评估爆破效果的可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
