StreetSurfaceVis:由众包的街道层面图像组成的数据集,按路面类型和质量进行注释
Alexandra Kapp1, Edith Hoffmann2, Esther Weigmann2
1Hochschule für Technik und Wirtschaft Berlin (HTW Berlin), Berlin, Germany. ak@3s.tu-berlin.de.
Scientific data
|January 17, 2025
概括
这项研究介绍了StreetSurfaceVis,这是一种用于道路表面评估的新数据集. 它使用新的抽样策略来创建平衡的数据集,提高道路质量和类型检测的模型准确性.
科学领域:
- 计算机科学 计算机科学
- 土木工程 土木工程是指土木工程.
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 道路的不均性对交通参与者构成重大风险,特别是像骑自行车和轮椅使用者这样的弱势道路使用者.
- 准确的道路表面评估对于提高交通安全和用户舒适性至关重要.
- 现有的数据集可能缺乏强大的道路表面分析模型所需的多样性和平衡性.
研究的目的:
- 介绍StreetSurfaceVis,这是一个新的,异质的数据集,包含9,122张街道平面图像,用于全面评估道路表面.
- 为了使强大的机器学习模型能够在各种图像来源中保持高精度.
- 为了应对道路表面数据集中不平衡的阶级分布的挑战.
主要方法:
- 收集并手动注释了9122张街道平面图像,以确定道路表面的类型和质量.
- 开发了一种新的抽样策略,以确保在不平衡的数据集中充分代表类别.
- 集成的外部标签预测资源,包括OpenStreetMap标签,自定义分类模型,基于提示的分类GPT-4o和图像嵌入相似性搜索.
主要成果:
- 成功创建了StreetSurfaceVis数据集,这是一个街道层面图像的异质集合.
- 证明了拟议的抽样策略在平衡类频率方面的有效性.
- 通过整合外部数据源和AI模型,显著减少了手动注释工作量.
结论:
- 街道表面Vis数据集为训练可靠的道路表面评估模型提供了宝贵的资源.
- 拟议的抽样策略有效地减轻了阶级不平衡问题,同时减少了注释工作.
- 结合多种数据丰富技术可以提高数据集的质量和代表性,从而提高模型性能.
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