使用支向量机器进行基于深度特征的裂纹检测:一项比较研究
K S Bhalaji Kharthik1, Edeh Michael Onyema2,3, Saurav Mallik4
1Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, 641112, India.
Scientific reports
|June 24, 2024
概括
使用传输学习深度卷积神经网络 (DCNNs) 进行自动破解检测,显著提高了基础设施的完整性. 这项研究比较了DCNN用于裂分类和特征提取,通过图像增强和支持矢量机集成来提高准确性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 基础设施的完整性至关重要,裂会带来重大风险.
- 手动检查裂的方法耗时且效率低下.
- 使用深度卷积神经网络 (DCNNs) 自动检测裂对于关键基础设施管理至关重要.
研究的目的:
- 为了比较传输学习的DCNN在裂检测方面的有效性.
- 评估DCNN作为分类模型和特征提取器.
- 评估图像增强和支持矢量机 (SVM) 集成对裂检测性能的影响.
主要方法:
- 在三个数据集 (SDNET,CCIC,BSD) 上评估了12个传输学习的DCNN模型.
- 应用了两种图像增强技术来改善SDNET数据集上的裂检测.
- 从DCNN中提取深度特征,以训练支持向量机 (SVM) 模型.
主要成果:
- 在SDNET上,ResNet101实现了53.40%的准确性;在BSD和CCIC上,EfficientNetB0 (98.8%) 和ResNet50 (99.8%) 分别表现出色.
- 图像增强显著提高了SDNET数据集上的转移学习DCNN模型的准确性.
- 将深度功能与SVM集成,在所有DCNN-数据集组合中提高了检测准确性.
结论:
- 转移学习的DCNN为自动破解检测提供了一个强大的方法.
- 使用SVM的图像增强和特征提取进一步提高了检测性能.
- 这项研究为提高基础设施检查效率和完整性提供了有价值的见解.
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