Surrounding rock classification from onsite images with deep transfer learning based on EfficientNet

authored by: Xiaoying Zhuang, Wenjie Fan, Hongwei Guo, Xuefeng Chen, Qimin Wang
Abstract: This paper proposes an accurate, efficient and explainable method for the classification of the surrounding rock based on a convolutional neural network (CNN). The state-of-the-art robust CNN model (EfficientNet) is applied to tunnel wall image recognition. Gaussian filtering, data augmentation and other data pre-processing techniques are used to improve the data quality and quantity. Combined with transfer learning, the generality, accuracy and efficiency of the deep learning (DL) model are further improved, and finally we achieve 89.96% accuracy. Compared with other state-of-the-art CNN architectures, such as ResNet and Inception-ResNet-V2 (IRV2), the presented deep transfer learning model is more stable, accurate and efficient. To reveal the rock classification mechanism of the proposed model, Gradient-weight Class Activation Map (Grad-CAM) visualizations are integrated into the model to enable its explainability and accountability. The developed deep transfer learning model has been applied to support the tunneling of the Xingyi City Bypass in the high mountain area of Guizhou, China, with great results.
Organisation(s): Institute of Photonics
External Organisation(s): Tongji University
Guizhou Xingyi Huancheng Expressway Co., Ltd.
Type: Article
Journal: Frontiers of Structural and Civil Engineering
Volume: 18
Pages: 1311-1320
No. of pages: 10
ISSN: 2095-2430
Publication date: 09.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Civil and Structural Engineering, Architecture
Electronic version(s): https://doi.org/10.1007/s11709-024-1134-7 (Access: Closed)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{6869e6450606456d8b84ec47aa839668,
title = "Surrounding rock classification from onsite images with deep transfer learning based on EfficientNet",
abstract = "This paper proposes an accurate, efficient and explainable method for the classification of the surrounding rock based on a convolutional neural network (CNN). The state-of-the-art robust CNN model (EfficientNet) is applied to tunnel wall image recognition. Gaussian filtering, data augmentation and other data pre-processing techniques are used to improve the data quality and quantity. Combined with transfer learning, the generality, accuracy and efficiency of the deep learning (DL) model are further improved, and finally we achieve 89.96% accuracy. Compared with other state-of-the-art CNN architectures, such as ResNet and Inception-ResNet-V2 (IRV2), the presented deep transfer learning model is more stable, accurate and efficient. To reveal the rock classification mechanism of the proposed model, Gradient-weight Class Activation Map (Grad-CAM) visualizations are integrated into the model to enable its explainability and accountability. The developed deep transfer learning model has been applied to support the tunneling of the Xingyi City Bypass in the high mountain area of Guizhou, China, with great results.",
keywords = "convolutional neural network, EfficientNet, Gradient-weight Class Activation Map, surrounding rock classification",
author = "Xiaoying Zhuang and Wenjie Fan and Hongwei Guo and Xuefeng Chen and Qimin Wang",
note = "Publisher Copyright: {\textcopyright} Higher Education Press 2024.",
year = "2024",
month = sep,
doi = "10.1007/s11709-024-1134-7",
language = "English",
volume = "18",
pages = "1311--1320",
journal = "Frontiers of Structural and Civil Engineering",
issn = "2095-2430",
publisher = "Springer Science + Business Media",
number = "9",
}

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