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一个混合深度学习模型用于增强结构损害检测:集成ResNet50,googLeNet和注意力机制.

Vikash Singh1, Anuj Baral1, Roshan Kumar2

  • 1Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Udupi 576104, India.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
概括

一个新的混合深度学习模型显著提高了结构损坏检测的准确性. 这种人工智能方法为基础设施安全和灾害响应提供了更快,更可靠的替代手工检查.

关键词:
这就是为什么CBAM是CBAM.在美国,CNN是CNN.在谷歌的网络上,谷歌LeNet.这就是ResNet-50的特点.检测损坏检测损坏的检测.深度学习是一种深度学习.

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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:

  • 基础设施安全至关重要,特别是在灾难发生后.
  • 手动损坏评估耗时且容易出现错误.
  • 需要先进的计算方法来有效地监测结构完整性.

研究的目的:

  • 开发一种先进的深度学习模型,用于准确检测结构损坏.
  • 为此任务增强现有的深度学习架构的性能.
  • 为基础设施维护和灾害响应提供强大的解决方案.

主要方法:

  • 开发了一个混合深度学习模型,将ResNet50和GoogLeNet结合起来.
  • 集成了一个卷积块注意模块 (CBAM) 来提高性能.
  • 一个多样化的图像数据集与数据增强被用于培训和验证.

主要成果:

  • 与独立的ResNet50和GoogLeNet相比,混合型号表现出优异的性能.
  • 关键指标包括精度,回忆,F1得分和准确性得到了显著改善.
  • 该模型在区分受损结构和未受损结构方面被证明是有效的.

结论:

  • 拟议的混合深度学习模型为结构损坏检测提供了高度准确和高效的解决方案.
  • 这种人工智能驱动的方法在可靠性和速度方面超过了传统方法.
  • 该模型非常适合用于灾害管理和基础设施维护的实时应用.