SUT-Crack:一个全面的数据集,用于在所有方法中检测路面裂
Mohammadreza Sabouri1, Alireza Sepidbar1
1Department of Civil Engineering, Sharif University of Technology, Tehran, Iran.
Data in brief
|October 16, 2023
概括
SUT-Crack数据集提供高质量的青路面裂图像,用于深度学习. 它包括详细的注释和地理标签,解决强大的裂检测现实世界的挑战.
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:
- 路面应急检测对于基础设施维护至关重要.
- 现有的数据集可能缺乏裂类型和环境条件的多样性.
- 深度学习模型需要全面的数据集来进行准确的路面裂分析.
研究的目的:
- 介绍SUT-Crack数据集用于青路面裂检测.
- 为培训和评估深度学习模型提供高质量,多样化的数据集.
- 促进裂分类,物体检测和细分方面的研究.
主要方法:
- 收集了130张青路面裂的高分辨率图像.
- 嵌入了具有挑战性的条件,如油污,阴影和不同的照明.
- 为细分和对象检测生成精确的地面真实注释.
- 将图像细分成超过25,000个200x200像素的样本,用于分类.
- 包括地理背景的地理标签和用于尺度校准的固定摄像头高度.
主要成果:
- SUT-Crack数据集解决了路面裂检测中的常见挑战.
- 注释是精确的,提高了深度学习模型的可靠性.
- 地理标记使空间分析和路面状况的绘制成为可能.
- 该数据集支持多个深度学习任务,包括分类,对象检测和细分.
结论:
- SUT-Crack是推进自动化路面裂检测的宝贵资源.
- 数据集的特点有助于开发更准确,更强大的基础设施监控人工智能系统.
- 它的综合性和现实世界的应用性使其适用于各种研究和实际应用.
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