Rock classification is a significant branch of geology which can help understand the formation and evolution of the planet, search for mineral resources, and so on. In traditional methods, rock classification is usually done based on the experience of a professional. However, this method has problems such as low efficiency and susceptibility to subjective factors. Therefore, it is of great significance to establish a simple, fast, and accurate rock classification model. This paper proposes a fine-grained image classification network combining image cutting method and SBV algorithm to improve the classification performance of a small number of fine-grained rock samples. The method uses image cutting to achieve data augmentation without adding additional datasets and uses image block voting scoring to obtain richer complementary information, thereby improving the accuracy of image classification. The classification accuracy of 32 images is 75%, 68.75%, and 75%. The results show that the method proposed in this paper has a significant improvement in the accuracy of image classification, which is 34.375%, 18.75%, and 43.75% higher than that of the original algorithm. It verifies the effectiveness of the algorithm in this paper and at the same time proves that deep learning has great application value in the field of geology.

中文翻译：

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基于深度学习的细粒度岩石图像分类研究

岩石分类是地质学的一个重要分支，可以帮助了解地球的形成和演化，寻找矿产资源等。在传统方法中，岩石分类通常是根据专业人士的经验来完成的。但该方法存在效率低、易受主观因素影响等问题。因此，建立简单、快速、准确的岩石分类模型具有重要意义。本文提出了一种结合图像切割方法和SBV算法的细粒度图像分类网络，以提高少量细粒度岩石样品的分类性能。该方法使用图像切割实现数据增强，无需添加额外的数据集，并使用图像块投票评分获得更丰富的补充信息，从而提高图像分类的准确性。32张图像的分类准确率分别为75%、68.75%和75%。结果表明，本文提出的方法在图像分类准确率上有显着提升，比原算法分别提高了34.375%、18.75%和43.75%。验证了本文算法的有效性，同时证明了深度学习在地质领域具有很大的应用价值。