爆堆块度分布的快速计算方法一直是学术界和工业界关注的热点，对于优化爆破效果、节约采矿成本具有重要意义。通过贴近摄影测量技术获取露天矿爆堆块体图片，构建超高清正射数据集，并提出基于深度学习的爆堆块度识别算法对爆破效果及优化采矿成本进行评估；引入可切换空洞卷积和递归特征金字塔改进模块提取不同岩体块度特征，利用傅里叶描述子建立爆堆块体统计并采用累计通过体积曲线替代累计通过率。结果表明，这种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具备有效性，目标爆堆的表面粉矿率均值为8.90 %，表面大块率均值为4.69 %。粉矿率偏高，大块率较低，需进一步优化爆破参数，降低成本。

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 mining costs. In this study, the high-resolution orthophoto datasets of open-pit mine blast piles were acquired 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 utilized 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 reduced.