多类混凝土缺陷分类使用指导语义空间融合和挤压刺激增强密度网模型
Ali Mahmoud Mayya1, Nizar Faisal Alkayem2,3
1Computer and Automatic Control Engineering Department, Faculty of Mechanical and Electrical Engineering, Latakia University, Latakia 2230, Syria.
Materials (Basel, Switzerland)
|December 31, 2025
概括
这项研究引入了一个新的深度学习框架,用于检测多个具体缺陷. 改进后的模型显著提高了结构完整性评估的分类准确性.
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
- 材料科学 材料科学 材料科学
背景情况:
- 混凝土结构容易出现影响安全和维护的缺陷.
- 准确的缺陷检测和量化对于结构健康监测至关重要.
- 现有的深度学习方法往往缺乏多类缺陷识别能力.
研究的目的:
- 开发一个先进的深度学习框架,用于多类混凝土缺陷检测和定位.
- 提高自动混凝土缺陷分类的准确性和可靠性.
- 创建一种用于非破坏性测量混凝土缺陷的实用工具.
主要方法:
- 在五个类别中编制了2029个混凝土缺陷图像的新型数据集.
- 通过引导语义空间融合模块和挤压激发架构增强了DenseNet201模型.
- 整合了注意力机制,以改善特征表示和缺陷区域跟踪.
主要成果:
- 拟议的框架比原始的DenseNet201模型实现了5.6%的精度改进.
- 实验验证证明了该模型在多类缺陷识别方面的优越性.
- 开发的模型有效地检测和定位各种混凝土缺陷.
结论:
- 增强的深度学习框架为多类混凝土缺陷检测提供了卓越的性能.
- 该研究提供了一种可靠的方法,用于非破坏性测量和分类混凝土缺陷.
- 将其集成到图形用户界面中,有助于在结构维护中的实际应用.
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