| 基于深度学习的智能矿物识别方法研究 |
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| 引用本文： | 郭艳军,周哲,林贺洵,刘小辉,陈丹丘,祝佳琪,伍峻琦.基于深度学习的智能矿物识别方法研究[J].地学前缘,2020,27(5):39-47. |
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| 作者姓名： | 郭艳军 周哲 林贺洵 刘小辉 陈丹丘 祝佳琪 伍峻琦 |
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| 作者单位： | 1.北京大学 地球与空间科学学院, 北京 1008712.北京大学 信息科学技术学院, 北京 1008713.北京大学 软件与微电子学院, 北京 1026004.北京大学 地球科学国家级实验教学示范中心, 北京 1008715.北京大学 地球科学国家级虚拟仿真实验教学中心, 北京 100871 |
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| 基金项目： | 国家科技重大专项(2017ZX0513-002);教育部产学合作协同育人项目(201802267003);中央高校改善基本办学条件专项基金项目(XG2001221);北京大学本科教学改革项目(JG1901221) |
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| 摘 要： | 矿物识别在许多研究领域都有着重要作用,基于深度学习技术的智能矿物识别为这些领域带来了新的发展方向,不仅能有效节省人工成本,还能减小识别错误。针对石英、角闪石、黑云母、石榴石和橄榄石共5种矿物进行实验,提出了一种准确高效的智能矿物识别方法。实验采用图像分析常用的卷积神经网络建立模型,设计出一套基于残差神经网络的矿物识别方法。本实验独立采集了5种矿物的偏光显微图像数据集,用于模型的训练、验证和测试,并通过合理的数据增强策略来扩充训练数据集。在卷积神经网络的结构设计上,选取了ResNet-18作为框架,最终于模型测试中取得89%的准确率,成功训练出一个较为精准的矿物识别模型,实现了基于深度学习的智能矿物识别方法。
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| 关 键 词： | 深度学习 矿物识别 计算机视觉 卷积神经网络 残差神经网络 |
| 收稿时间： | 2020-03-30 |
The mineral intelligence identification method based on deep learning algorithms |
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| GUO Yanjun,ZHOU Zhe,LIN Hexun,LIU Xiaohui,CHEN Danqiu,ZHU Jiaqi,WU Junqi.The mineral intelligence identification method based on deep learning algorithms[J].Earth Science Frontiers,2020,27(5):39-47. |
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| Authors: | GUO Yanjun ZHOU Zhe LIN Hexun LIU Xiaohui CHEN Danqiu ZHU Jiaqi WU Junqi |
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| Institution: | 1. School of Earth and Space Sciences, Peking University, Beijing 100871, China2. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China3. School of Software & Microelectronics, Peking University, Beijing 102600, China4. National Experimental Teaching Demonstrating Center of Earth Sciences(Peking University), Beijing 100871, China5. National Virtual Simulation Experimental Teaching Center of Earth Sciences(Peking University), Beijing 100871, China |
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| Abstract: | Mineral classification plays an important role in many research fields. Intelligent mineral identification based on deep learning brings a new development direction to these fields, it can effectively save labor costs as well as reducing classification errors. The purpose of this paper is to study an accurate, efficient and versatile intelligent mineral identification method by deep learning. We trained and tested this method on five kinds of minerals: quartz, hornblende, biotite, garnet and olivine. We used the convolution neural network, commonly applies to image analysis, to establish the model and designed the model structure based on residual network (ResNet). In order to support deep learning, we collected microscopic imaging data sets of five kinds of minerals independently, and used them to train, verify and test the model. Besides, we also expanded the data sets for training through reasonable data augmentation. In terms of structural design of the convolutional neural network, we selected ResNets-18 as the framework and finally trained a successful mineral identification model achieving 89% accuracy in the test. |
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| Keywords: | deep learning mineral classification computer vision convolutional neural network residual neural network |
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