矿业科学学报2025，Vol.10Issue(1)：151-162,12.DOI:10.19606/j.cnki.jmst.2024941
基于深度学习和贴近摄影测量的露天矿爆堆块度识别算法
Blast pile fragments recognition algorithm for open pit mines based on deep learning and nap-of-the-object photogrammetry
摘要
Abstract
The rapid calculation of blast pile fragment size distribution has been a focal point in both academia and industry due to its significant application on optimizing blasting effects and reducing min-ing costs.In this study,the high-resolution orthophoto datasets of open-pit mine blast piles were ac-quired using nap-of-the-object photogrammetry techniques,and a deep learning algorithm for fragment size distribution recognition was proposed to assess blasting effectiveness and optimize mining costs.To enhance the feature extraction of different rock fragmentation sizes,we introduced a switchable atrous convolution module and a recursive feature pyramid refinement module.Fourier descriptors were uti-lized to establish statistical distributions of the blast piles,while the cumulative passing volume curve was employed in place of the cumulative passing rate.The results demonstrated the effectiveness of the proposed algorithm:the mean fine fragmentation rate on the surface of the target blast pile was 8.90%,and the mean large block rate on the surface was 4.69%.The high fine fragmentation rate and low large block rate indicate that the blasting parameters can be further optimized,and the cost can be re-duced.关键词
爆堆块度/深度学习/贴近摄影测量/机器视觉Key words
blast pile fragments/deep learning/nap-of-the-object photogrammetry/machine vision分类
矿山工程引用本文复制引用
陈承桢,李荟,朱万成,牛雷雷..基于深度学习和贴近摄影测量的露天矿爆堆块度识别算法[J].矿业科学学报,2025,10(1):151-162,12.基金项目
国家重点研发计划(2022YFC2903901,2022YFC2903903) （2022YFC2903901,2022YFC2903903）
国家自然科学基金(52304167) （52304167）
中央高校基本科研业务费专项资金(N2301020) （N2301020）
辽宁省自然科学基金联合基金(2023-MSBA-122) （2023-MSBA-122）
