TIGPR:一个多视图地面穿透雷达检测数据,用于对运输基础设施的损害评估
Zhen Liu1, Bingyan Cui2, Xingyu Gu1
1Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China.
Data in brief
|June 11, 2025
概括
TIGPR数据集提供高质量的地面透雷达 (GPR) 图像,用于检测运输基础设施损坏. 该集合有助于开发用于自动化条件监测和非破坏性测试的机器学习模型.
科学领域:
- 土木工程 土木工程是指土木工程.
- 地质物理学 地质物理学
- 计算机科学 计算机科学
背景情况:
- 交通基础设施需要持续监测损坏情况.
- 地底透雷达 (GPR) 是用于地下评估的关键非破坏性测试 (NDT) 方法.
- 现有的数据集可能缺乏用于基础设施监控中先进机器学习应用所需的多样性和质量.
研究的目的:
- 介绍TIGPR数据集,这是一个全面的GPR图像集合,用于检测运输基础设施损坏.
- 为开发和评估用于自动化损害评估的深度学习模型提供基准数据集.
- 支持道路,桥梁,道和机场智能监控系统的发展.
主要方法:
- 从多个地点和基础设施类型的实验室和实地调查中收集了各种GPR图像.
- 使用二维和三维GPR系统 (IDS-FastWave,MALA GX750,GeoScope 3D-Radar) 来进行数据采集.
- 将GPR数据处理成B扫描和C扫描图像,具有指定分辨率和真实世界的覆盖面积.
主要成果:
- TIGPR数据集包含高质量的GPR图像,捕捉各种基础设施损坏,如裂,脱,松和空隙.
- 图像是针对深度学习任务的结构,包括分类,对象检测和语义细分.
- 该数据集涵盖了各种环境条件和基础设施类型,确保了广泛的适用性.
结论:
- TIGPR数据集是推动交通基础设施智能损害检测的宝贵资源.
- 它有助于开发可靠的机器学习模型,用于自动化NDT和条件监控.
- 这一数据集将加速智能基础设施管理和维护的研发.
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