Blasted rock material serves a critical role in various engineering applications, yet the phenomenon of segregation-where particle sizes vary significantly along the gradient of a quarry pile-presents challenges for optimizing quarry material storage and handling. This study introduces an advanced image analysis methodology to characterize such segregation of rock fragments. The accurate delineation of detailed rock fragment size distributions was achieved through the analysis of drone-captured imagery, coupled with the application of an enhanced Unet semantic segmentation model integrated with an expansion-based post-processing technique. The quarry slope was stratified into four vertical sections, with the size distribution of each section quantified via ellipsoid shape approximations. Our results disclose pronounced vertical segregation patterns, with finer particles concentrated in the upper slope regions and coarser particles in the lower. Utilizing relative characteristic diameters, we offered insight into the degree of segregation, thereby illustrating the spatial heterogeneity in fragment size more clearly. The techniques outlined in this study deliver a scalable and accurate method for assessing fragment size distribution, with the potential to better inform resource management and operational decisions in quarry management.

翻译：爆破岩石材料在各类工程应用中扮演着关键角色，然而分离现象——即沿采石场堆料梯度方向粒径显著变化——对优化采石场材料存储与处理提出了挑战。本研究提出了一种先进的图像分析方法，用以表征岩石碎块的此类分离特征。通过分析无人机航拍图像，结合增强型Unet语义分割模型与基于膨胀的后处理技术，实现了岩石碎块详细粒径分布的精确描绘。采石场斜坡被划分为四个垂直区域，各区域粒径分布通过椭球形状近似进行量化。研究结果揭示了显著的垂直分离模式：细颗粒集中于斜坡上部区域，而粗颗粒分布于下部区域。利用相对特征粒径，我们深入解析了分离程度，从而更清晰地展示了碎块粒径的空间异质性。本研究所提出的技术提供了一种可扩展且精确的粒径分布评估方法，有望为采石场管理中的资源优化与运营决策提供更有效的参考依据。