In mining operations, blast-induced rock fragmentation affects the productivity and efficiency of downstream operations including digging, hauling, crushing, and grinding. Continuous measurement of rock fragmentation is essential for optimizing blast design. Current methods of rock fragmentation analysis rely on either physical screening of blasted rock material or image analysis of the blasted muckpiles; both are time consuming. This study aims to present and evaluate the measurement of rock fragmentation using deep learning strategies. A deep neural network (DNN) architecture was used to predict characteristic sizes of rock fragments from a 2D image of a muckpile. The data set used for training the DNN model is composed of 61,853 labelled images of blasted rock fragments. An exclusive data set of 1,263 labelled images were used to test the DNN model. The percent error for coarse characteristic size prediction ranges within $\pm$25% when evaluated using the test set. Model validation on orthomosaics for two muckpiles shows that the deep learning method achieves a good accuracy (lower mean percent error) compared to manual image labelling. Validation on screened piles shows that the DNN model prediction is similar to manual labelling accuracy when compared with sieving analysis.

在采矿作业中，爆炸引起的岩石破碎会影响下游作业的生产力和效率，包括挖掘、运输、破碎和研磨。连续测量岩石碎裂对于优化爆破设计至关重要。当前的岩石碎裂分析方法依赖于爆破岩石材料的物理筛选或爆破渣土的图像分析；两者都很耗时。本研究旨在展示和评估使用深度学习策略对岩石破碎的测量。一种深度神经网络 (DNN) 架构被用于预测来自垃圾堆的 2D 图像的岩石碎片的特征尺寸。用于训练 DNN 模型的数据集由 61,853 张爆破岩石碎片的标记图像组成。使用包含 1,263 个标记图像的专有数据集来测试 DNN 模型。粗特征尺寸预测范围内的百分比误差$\pm$使用测试集评估时为 25%。两个 muckpiles 的正射镶嵌模型验证表明，与手动图像标记相比，深度学习方法实现了良好的准确性（较低的平均百分比误差）。对筛选桩的验证表明，与筛分分析相比，DNN 模型预测与手动标记精度相似。