基于 EfficientNet 的深度迁移学习从现场图像中进行围岩分类

IF 2.9 3区 工程技术 Q2 ENGINEERING, CIVIL
Xiaoying Zhuang, Wenjie Fan, Hongwei Guo, Xuefeng Chen, Qimin Wang
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引用次数: 0

摘要

本文提出了一种基于卷积神经网络(CNN)的准确、高效和可解释的围岩分类方法。最先进的鲁棒 CNN 模型(EfficientNet)被应用于隧道壁图像识别。采用高斯滤波、数据增强和其他数据预处理技术来提高数据质量和数量。结合迁移学习,进一步提高了深度学习(DL)模型的通用性、准确性和效率,最终实现了 89.96% 的准确率。与其他最先进的 CNN 架构(如 ResNet 和 Inception-ResNet-V2(IRV2))相比,本文提出的深度迁移学习模型更加稳定、准确和高效。为了揭示所提模型的岩石分类机制,梯度-权重类激活图(Gradient-weight Class Activation Map,Grad-CAM)可视化技术被集成到模型中,使其具有可解释性和责任性。所开发的深度迁移学习模型已被应用于中国贵州高山地区兴义绕城高速的隧道施工,并取得了良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Surrounding rock classification from onsite images with deep transfer learning based on EfficientNet

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.

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来源期刊
CiteScore
5.20
自引率
3.30%
发文量
734
期刊介绍: Frontiers of Structural and Civil Engineering is an international journal that publishes original research papers, review articles and case studies related to civil and structural engineering. Topics include but are not limited to the latest developments in building and bridge structures, geotechnical engineering, hydraulic engineering, coastal engineering, and transport engineering. Case studies that demonstrate the successful applications of cutting-edge research technologies are welcome. The journal also promotes and publishes interdisciplinary research and applications connecting civil engineering and other disciplines, such as bio-, info-, nano- and social sciences and technology. Manuscripts submitted for publication will be subject to a stringent peer review.
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