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有色金属工程:2024,14(3)
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小样本下结合X射线透射技术的数据增强型矿石分类方法
王文1, 何剑锋2,2,1
(1.东华理工大学江西省核地学数据科学与系统工程技术研究中心江西 南昌;2.东华理工大学信息工程学院江西 南昌)
Small-sample ore classification algorithm based on X-ray transmission technology with data augmentation
wangwen1, hejianfeng2
(1.Information Engineering College,East China University of Technology,Nanchang;2.1.Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System,East China University of Technology,Nanchang,330013,Jiangxi,China 2. Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology,East China University of Technology,Nanchang,330013,Jiangxi,China 3.Information Engineering College,East China University of Technology,Nanchang 330013,China)
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投稿时间:2023-08-30    修订日期:2023-11-29
中文摘要: 摘 要:目的 针对工业领域利用深度学习模型对矿石进行在线分选时,矿石样本稀少导致的模型过拟合、分类准确率低等问题,提出一种结合X射线透射成像技术的矿石数据增强分类方法。该方法基于改进辅助生成对抗网络(Enhance-based Classification ACGAN-gp, EC-ACGAN-gp),可实现对矿石品位的精准预测。方法 首先,在模型架构上,判别器和生成器采用卷积和连续残差块构建,引入注意力机制捕捉矿石细节特征,并使用Wasserstein距离和梯度惩罚重构判别器的损失函数,以提高对抗训练的稳定性,避免模式崩溃。其次,在算法上,增加辅助分类器与生成器协同训练,生成高质量样本并扩充原始数据集。最后利用判别器对矿石样本进行类别预测。结果 实验结果表明,模型分类准确率可达89.62%。结论 所提方法可有效提高小样本条件下矿石品位的分类精度,模型生成的矿石样本泛化性良好,对提高其他识别网络在矿石品位预测方面的性能均适用。
中文关键词: 小样本分类  数据增强  ACGAN  X射线成像
Abstract:Due to the limited availability of ore samples and the issues of overfitting and low classification accuracy in online sorting of ores using deep learning models in the industrial field, a mining data augmentation classification method is proposed that combines X-ray transmission imaging technology. The method is based on an improved Auxiliary Classifier Generative Adversarial Network (ACGAN-gp), which enables accurate prediction of ore grade. Firstly, in terms of the model architecture, the discriminator and generator are constructed using convolutional and continuous residual blocks. An attention mechanism is introduced to capture the detailed features of the ores. The loss function of the discriminator is reconstructed using the Wasserstein distance and gradient penalty to enhance the stability of adversarial training and prevent mode collapse. Secondly, in terms of the algorithm, an auxiliary classifier is added to the training process, which collaborates with the generator to generate high-quality samples and augment the original dataset. Finally, the discriminator is used for classifying the ore samples. Experimental results demonstrate that the proposed method achieves an impressive classification accuracy of 89.62%. The proposed method effectively improves the classification accuracy of ore grades under the condition of limited samples. The generated ore samples by the model exhibit good generalization, and the method can also enhance the performance of other recognition networks in ore grade prediction.
文章编号:YSJSGC20230517     中图分类号:    文献标志码:
基金项目:国家自然科学基金资助项目(11865002,U2067202);江西省主要学科学术和技术带头人培养计划(20225BCJ22004);江西省重点研发计划(20203BBG73069)。
引用文本:
王文,何剑锋.小样本下结合X射线透射技术的数据增强型矿石分类方法[J].有色金属工程,2024,14(3):.

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