跨越卷积神经网络和变压器,以有效检测混凝土建筑结构中的裂
Dhirendra Prasad Yadav1, Bhisham Sharma2, Shivank Chauhan1
1Department of Computer Engineering & Applications, G.L.A. University, Mathura 281406, India.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
一个新的卷积和复合注意力变压器网络 (CCTNet) 模型改善了建筑裂检测. 这种先进的方法比传统的结构完整性评估技术提供了更高的精度和效率.
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:
- 在建筑物中精确的裂检测对于安全,寿命和经济价值至关重要.
- 传统的卷积神经网络 (CNN) 破裂检测方法存在局限性,包括高计算成本和浅层特征提取.
- 现有的深度学习 (DL) 技术可能无法完全捕捉复杂的裂特性.
研究的目的:
- 引入一种新的卷积和复合注意力变压器网络 (CCTNet) 模型,用于在建筑结构中增强裂检测.
- 解决传统CNN的局限性,例如高计算成本和不充分的特征表示.
- 提高建筑环境中裂识别的准确性,效率和可靠性.
主要方法:
- 开发了一个新的CCTNet模型,整合了卷积,通道注意力和基于窗口的自我注意力机制.
- 利用改进的交叉注意模块来增强跨窗口的功能交互和集成.
- 利用局部特征提取 (CNN) 和全球上下文理解 (自我注意) 两种方式.
主要成果:
- CCTNet实现了高精度率:98.60%在历史建筑裂2019,98.93%在SDTNET2018,和99.33%在拟议的DS3数据集.
- 该模型展示了接近零的训练验证损失,表明有效的学习.
- 曲线下的实现面积 (AUC) 为历史建筑裂2019的0.99分和SDTNET2018的0.98分.
结论:
- 拟议的CCTNet模型显著优于现有的建筑裂检测方法.
- CCTNet提供了一个准确,高效和可靠的解决方案,用于识别结构裂.
- 该模型为建筑环境中的自动裂检测设定了新的基准.
相关概念视频
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