当地-全球特征适应融合网络用于建筑裂检测
Yibin He1, Zhengrong Yuan1, Xinhong Xia1
1Hunan Architectural Design Institute Group Co., Ltd., Changsha 410082, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
一个新的网络通过结合本地和全球细节,有效地检测建筑裂. 这种方法通过使用先进的功能融合来提高裂识别精度来提高结构安全性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 裂是一种常见的结构损伤,需要及时检测以确保建筑安全.
- 有效的裂检测需要整合当地细节和全球背景.
- 现有的方法可能会在不同的裂复杂性和背景相似性方面扎.
研究的目的:
- 提出一个新的网络,用于准确检测建筑结构中的裂.
- 开发一种有效地融合本地和全球特征的方法,以提高性能.
- 引入一个新的数据集,用于评估裂纹检测算法.
主要方法:
- 开发了一个局部-全球特征自适应融合网络 (LGFAF-Net).
- 该网络使用VMamba编码器进行全球特征提取,并使用残余网络进行本地特征提取.
- 一个多功能自适应融合 (MFAF) 模块集成了这两个分支的功能.
主要成果:
- 拟议的LGFAF-Net在裂检测方面表现出卓越的性能.
- 对新的建筑外墙裂 (BEWC) 数据集和公共数据集的实验证实了该方法的有效性.
- 双编码网络和自适应融合模块显著提高了检测精度.
结论:
- 该LGFAF-Net有效地整合了本地和全球信息,以进行可靠的裂检测.
- 拟议的方法在自动化结构健康监测方面取得了重大进展.
- BEWC数据集为未来裂纹检测研究提供了宝贵的资源.
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