Abstract
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.
Highlights
Rocks are naturally occurring minerals or solid aggregates composed of minerals and other materials [1]
SE-ResNet-50 [17] achieved more useful features and worked better than other same-type networks, Vision Transformer (ViT) [18] transformed the image classification problem into natural language processing (NLP) problem and achieved the state-of-the-art levels on multiple datasets, EfficientNet-B0 [19] is a model with leading speed and higher accuracy, and they are all the mainstream backbone network modules. erefore, SE-ResNet-50 and EfficientNet-B0 were selected, respectively, as backbone network modules to perform rock image classification experiments on the new framework to validate the performance of the new model
E experimental results in Table 5 show that the accuracy of the SE-ResNet-50 and ViT model using the image cutting method decreased by 12.5% and 6.25%, respectively, and the EfficientNet-B0 model decreased by 3.125%, indicating that the use of image cutting to increase the dataset alone does not improve the classification accuracy of small-sample fine-grained images. e accuracy of the SEResNet-50, ViT, and EfficientNet-B0 models using scoring by voting (SBV) algorithm alone improves by 31.25%, 6.25%, and 43.75%, respectively, indicating that the SBV algorithm can obtain more messages to help classification and effectively improve the classification accuracy of fine-grained images with fewer samples
Summary
Rocks are naturally occurring minerals or solid aggregates composed of minerals and other materials (volcanic glass, biological bones, rock debris, etc.) [1]. Lin et al [7] used a deep learning method based on a convolutional neural network for rock recognition, and the classification accuracy rate of 15 kinds of common rock image data could reach 63%. We distribute the classification results according to the proportion of the filling area, completing the augmentation of the training set data It has some advantages: noninformation pixels will not appear in the training process, which can improve training efficiency; it retains the advantages of data regional dropout; by requiring the model to recognize the object from the partial view and adding other pieces of sample information to the cleared area, the positioning ability of the model can be further enhanced, and so on.
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