Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang, 110819, China.
China Railway 19th Bureau Group Mining Investment Co., Ltd., Beijing, 100161, China.
PLoS One. 2023 Sep 14;18(9):e0291115. doi: 10.1371/journal.pone.0291115. eCollection 2023.
Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is inefficient and inaccurate. This study proposes the U-CARFnet and U-Net models for segmenting blasting fragment images from open-pit mines based on an attention mechanism, residual learning module, and focal loss function. It compares this technique with traditional image segmentation ones and a variety of deep learning models to verify the efficacy of the proposed model. Experimental results show that the accuracy of the U-CARFnet model proposed in this paper reaches 97.11% in the performance evaluation, which shows better performance than the traditional image segmentation method. In this study, the U-CARFnet model is used in the application, and a superior performance is obtained, with an average segmentation error of 5.46%. The proposed approach provides an effective technique for statistically analyzing images of mine rock.
硐室爆破是露天金属矿山的主要生产手段。爆破后形成的岩体块度大小对生产成本有重大影响。目前，矿石块度主要通过人工测量或肉眼估计获得，效率低且不准确。本研究提出了基于注意力机制、残差学习模块和焦点损失函数的 U-CARFnet 和 U-Net 模型，用于从露天矿爆破碎片图像中进行分割。将该技术与传统图像分割技术和各种深度学习模型进行比较，验证了所提出模型的有效性。实验结果表明，本文提出的 U-CARFnet 模型在性能评估中的准确率达到 97.11%，比传统图像分割方法具有更好的性能。在本研究中，应用了 U-CARFnet 模型，获得了优越的性能，平均分割误差为 5.46%。该方法为矿山岩石图像的统计分析提供了一种有效的技术。
