Open AccessAutomated Classification of High-resolution Rock Image Based on Residual Neural Network
Author(s) -
Weibo Cai,
Juncan Deng,
Qirong Lu,
Kengdong Lu,
Kaiqing Luo
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2095/1/012051
Subject(s) - artificial intelligence , residual , pattern recognition (psychology) , artificial neural network , computer science , image (mathematics) , image segmentation , segmentation , identification (biology) , residual neural network , contextual image classification , algorithm , botany , biology
The identification and classification of high-resolution rock images are significant for oil and gas exploration. In recent years, deep learning has been applied in various fields and achieved satisfactory results. This paper presents a rock classification method based on deep learning. Firstly, the high-resolution rock images are randomly divided into several small images as a training set. According to the characteristics of the datasets, the ResNet (Residual Neural Network) is optimized and trained. The local images obtained by random segmentation are predicted by using the model obtained by training. Finally, all probability values corresponding to each category of the local image are combined for statistics and voting. The maximum probability value and the corresponding category are taken as the final classification result of the classified image. Experimental results show that the classification accuracy of this method is 99.6%, which proves the algorithm’s effectiveness in high-resolution rock images classification.