Academic Journal of Computing & Information Science, 2022, 5(8); doi: 10.25236/AJCIS.2022.050801.
Quan Hao, Nan Nie, Fuxi Zhu
School of Information Engineering, Wuhan College, Wuhan, 430212, China
With substantial improvement of computer hardware performance and rapid development in artificial intelligence research, artificial intelligence recognition technology has been applied in many industries, such as medicine, education, industry, commerce, exploration and other professional fields. In geological exploration, the classification and identification of rock samples is an important link in geological analysis. However, the traditional manual classification faces high economic and time costs, and is vulnerable to subjective judgment which may affect the recognition result. In order to avoid these problems, research is carried out on the specific application of computer-based artificial intelligent recognition technology in recognition of rock images and the performance difference caused by different parameters. The sample data used in the experiment are lithic fragments and core samples capture by industrial cameras at the well logging site, with a total of 350 images which cover 7 types of rock samples. In this paper, the 350 rock images are divided into training set, validation set and test set based on certain proportion. Since the data set is too small and the quantity of each type of rocks varies, data augmentation technology is used to expand the original data before training. During the expansion, the quantity of different types of rock samples are balanced. At the training stage, the open-source deep learning framework Pytorch is used to construct a multi-layer convolutional neural network for learning and classification training on the training set. The best accuracy rate in the tests reaches 82.86%. However, the recognition rate is poor for stretched images, which required further optimization of the network structure. Based on the experimental results, the optimization orientations of neural network training are summarized, including improvement of data set quality, optimization of network structure, and adjustment of training strategies. In order to display the experimental data as intuitively as possible, data visualization tools such as PlotNeuralNet and Python matplotlib are used in the experiment for specific visualization work, after which the experimental data and model effect are analyzed from the perspective with combination of data and images.
in-depth learning; convolutional neural network; recognition of rock sample images
Quan Hao, Nan Nie, Fuxi Zhu. The Design and Realization of Intelligent Classification of Rock Samples Based on Convolutional Neural Network. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 8: 1-11. https://doi.org/10.25236/AJCIS.2022.050801.
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