通过卷积神经网络对脚的马赛克裂映射
Apichat Buatik1, Phromphat Thansirichaisree2, Phisutwat Kalpiyapun1
1Research Unit of Infrastructure Inspection, Monitoring, Repair and Strengthening, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.
Scientific reports
|April 3, 2024
概括
本研究介绍了一个深度卷积神经网络 (DCNN),用于使用无人机图像在大面积高效地检测混凝土裂. 这种新的系统达到95%的准确性,优于结构性健康监测的传统方法.
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:
- 混凝土结构需要经常检查,以监测结构健康.
- 现有的基于图像的裂检测仅限于单个图像,阻碍了大规模评估.
- 自动化裂检查提供了更高的效率和准确性.
研究的目的:
- 提出一个先进的基于图像的裂检测系统用于混凝土底座.
- 用无人机 (UAV) 拍摄图像来检查大面积的现有系统的局限性.
- 为了利用深度卷积神经网络 (DCNNs) 在马赛克图像中准确识别裂.
主要方法:
- 无人机拍摄的混凝土底座图像被获取并转化为3D模型.
- 从3D模型中生成了马赛克复合图像.
- 一个VGG16深度卷积神经网络 (DCNN) 在增强的224x224像素图像补丁上受过训练.
- 在复合图像上使用滑动窗口技术来定位裂.
主要成果:
- 拟议的DCNN系统实现了95%的混凝土裂检测精度.
- 与传统的基于特征的检测方法相比,该系统表现出更高的性能.
- 该方法有效地识别来自无人机数据的大型马赛克图像中的裂.
结论:
- 开发的基于DCNN的系统提供了一个高度准确和高效的解决方案,用于在广泛的地区检测混凝土裂.
- 这种方法显著改进了现有的混凝土基础设施结构健康监测方法.
- 使用无人机图像与DCNN相结合,在自动化结构检查方面呈现出有前途的进步.
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