基于卷积神经网络的沙多洛米特基础的沙化程度分类
Meiqian Wang1,2, Changxing Zhang1,2, Haiming Liu1,2
1Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China.
Scientific reports
|August 9, 2024
概括
对道的稳定性来说,分类沙多洛米特沙化是至关重要的. 卷积神经网络 (CNN) 模型使用新的大规模图像数据集实现了91.4%的准确性,改善了地质工程分析.
科学领域:
- 地质技术工程 地质技术工程
- 人工智能的人工智能
- 地质地质地质地质地质地
背景情况:
- 沙化降低了多洛米特的强度,影响了道的稳定性.
- 对于地质工程项目来说,将沙状多洛米特的沙化程度进行分类是具有挑战性的.
- 传统的分类方法往往耗时且不准确.
研究的目的:
- 引入基于卷积神经网络 (CNN) 的图像分类,用于沙多洛米特沙分类.
- 建立一个大规模的数据集,用于沙多洛米特的沙化分类.
- 评估CNN模型在此分类任务中的有效性.
主要方法:
- 开发一个大规模的数据集,包含5729张沙多洛米特的图像.
- 将图像分为四种不同的沙化度的分类.
- 基于CNN的图像分类模型的应用和实验.
主要成果:
- 在砂化度分类中,CNN模型实现了高达91.4%的高准确率.
- 已建立的数据集为研究和开发提供了宝贵的资源.
- 展示了人工智能在应对复杂地质挑战方面的潜力.
结论:
- 基于CNN的图像分类是沙丘多洛米特沙化的一个开创性和有效的方法.
- 开发的数据集和模型为地质工程提供了重大进展.
- 这种方法对复杂的地理分析和基础设施稳定性有广泛的影响.
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