用AI自动分析爆破后岩石碎片，实时生成精准分割与空间分布报告。
An Automated Deep Segmentation and Spatial-Statistics Approach for Post-Blast Rock Fragmentation Assessment
- 基于微调YOLO12l-seg模型实现快速实例分割，每秒处理15帧
- 提取主成分方向、密度热点、尺寸深度关系等四类空间特征
- 适用于野外现场快速评估，对密集小碎片也具有鲁棒性
我们提出一个端到端流程，采用在500多张标注爆破后图像上微调的YOLO12l-seg模型，实现每秒约15帧的实时实例分割（框[email protected] ~ 0.769，掩码[email protected] ~ 0.800）。高保真掩码转化为归一化3D坐标，进而提取多维度空间描述符：主成分方向、核密度热点、尺寸-深度回归关系及Delaunay边统计。通过四个典型案例展示关键碎块分布模式。实验验证了该框架在准确性、抗小目标密集干扰以及野外快速自动化评估方面的有效性。
原文摘要 · Abstract (English)
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 [email protected] ~ 0.769, Mask [email protected] ~ 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.
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