SDNET 2025-钢螺栓连接评估的注释数据集
Faezeh Jafari1, Sattar Dorafshan2
1Department of Civil Engineering, University of North Dakota, Grand Forks, ND 58202, USA.
Data in brief
|December 4, 2025
概括
本研究引入了一套新的数据集,用于检测使用无人机系统 (UAS) 的钢结构中有缺陷的螺栓. 该数据集有助于开发人工智能模型,用于对桥梁和杆子进行自主检查,改善结构安全.
科学领域:
- 结构工程 结构工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 像桥梁这样的钢结构的传统检查是劳动密集型,昂贵且不安全的.
- 缺陷的螺栓损害了结构完整性,并可能导致灾难性的故障.
- 现有的方法缺乏足够的数据来实现人工智能驱动的自主螺栓缺陷检测.
研究的目的:
- 为训练人工智能模型检测钢结构中螺栓和螺母缺陷提供全面的注释数据集.
- 为了解决公共数据集的稀缺性,用于自动化的螺栓连接评估.
- 促进人工智能的发展,以加强结构状况评估.
主要方法:
- 汇编了826张注释图片,显示了各种螺栓缺陷 (缺失,松动,固定).
- 通过在全球范围内对街道结构的真实视觉检查和受控的实验室样本收集数据.
- 用边界框和面具对图像进行注释,用于对象检测算法.
主要成果:
- 826个图像的多样化数据集,包括324个松散螺栓,200个缺失螺栓和302个固定螺栓.
- 数据集适用于各种对象检测算法,因为有界框和掩码注释.
- 数据的目的是通过各种收集方法来实现概括性.
结论:
- 这一数据集对于推进基于人工智能的钢结构自动检查至关重要.
- 它可以开发更强大,更准确的模型来检测有缺陷的螺栓连接.
- 该资源支持在结构性健康监测中提高安全性和效率.
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