@article{Liu2023, 
author = {Houxiang Liu and Jian Wang},
title = {Lithology Identification Method of Tunnel Surrounding Rock Based on Transfer Learning Technology},
year = {2023},
journal = {Chinese Journal of Underground Space and Engineering},
volume = {19},
number = {2},
pages = {437-445},
keywords = {tunnel engineering, lithology identification, transfer learning, sub-image method, inception-resnet-v2},
url = {https://www.sciopen.com/article/10.20174/j.juse.2023.02.010},
doi = {10.20174/j.juse.2023.02.010},
abstract = {In order to realize the automatic recognition and classification of the surrounding rock lithology of the tunnel, a method of lithology recognition based on migration learning technology is proposed. First, pre-training on the Image-Net dataset by using the Inception-ResNet-V2 (IRV2) convolutional neural network model, and using model transfer learning technology to retrain the rock image dataset (including granite, limestone, basalt and shale) to obtain The lithology recognition model of the surrounding rock of the tunnel; then, the IRV2 model is tested, and the recognition performance of the three models: ResNet-50, Inception-V3 and VGG16 is compared; finally, the sub-image method and the overall image method are performed Comparative test of recognition effect. The experimental results show that: (1) The various classification performance indicators of IRV2 are all the best, and all can reach more than 90%, indicating that the model can realize the effective identification and accurate classification of surrounding rock lithology; (2) For rock pictures with more prominent texture, structure and structure, the recognition performance of the model is better; (3) The sub-image method can effectively improve the model’s performance compared to the overall image method. Identify performance.}
}