一个由无人机收集的建筑表面缺陷的数据集,用于基于机器学习的检测
Qikai Zha1, Yiming Yao1, Yufan Zheng2
1School of Engineering, Anhui Agricultural University, Hefei, 230036, P. R. China.
Scientific data
|November 22, 2025
概括
这项研究引入了一个由无人机 (UAV) 捕获的建筑表面缺陷的新数据集. 本资源旨在改善民用基础设施的自动视觉检查和维护.
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:
- 传统的基础设施检查是劳动密集型和低效的.
- 缺乏大量高质量的数据集,阻碍了基础设施缺陷检测的深度学习.
研究的目的:
- 创建一个全面的,多样化的,公开可访问的基于无人机的数据集,用于建筑表面缺陷分析.
- 促进基础设施维护的自动化视觉评估和多任务学习的进步.
主要方法:
- 收集了14471张建筑表面的高分辨率无人机图像.
- 采集了六种结构类型和五种缺陷类别 (裂,脱落,泄漏,腐蚀,凸起) 的数据.
- 带有框框的注释图像,并分为培训,验证和测试集.
主要成果:
- 开发了一套大规模,多样化的建筑表面缺陷数据集.
- 数据集涵盖了各种条件,包括不同的环境和照明.
- 为缺陷检测,细分和自动评估提供了一个基准.
结论:
- 创建的数据集解决了对基础设施检查深度学习中高质量的数据的需求.
- 它是开发和评估自动化视觉评估工具的宝贵资源.
- 能够进行多任务研究,以改善民用基础设施的维护和管理.
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