Characterizing segregation in blast rock piles a deep-learning approach leveraging aerial image analysis
arxiv(2024)
摘要
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.
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