一个大规模的图像存储库,用于自动化路面应急分析和降解趋势预测
Hanlin Yang1, Jinpu Cao2, Jun Wan3
1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai, 201804, P. R. of China.
Scientific data
|August 14, 2025
概括
研究人员开发了一个大规模的路面应急数据集,用于自动检测. 这一数据集支持人工智能算法,用于改进道路维护策略和应急跟踪.
科学领域:
- 运输工程 运输工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 自动化路面应急检测对于运输基础设施管理至关重要.
- 深度学习和人工智能识别算法越来越多地使用,但需要大量,多样化的数据集.
- 现有的数据集缺乏足够的样本大小,类别多样性和应急跟踪能力.
研究的目的:
- 构建一个大规模的图像数据集,用于路面困境识别和长期跟踪.
- 为评估和改进人工智能驱动的路面危险检测算法提供可靠的数据.
- 支持动态监测和优化道路维护战略.
主要方法:
- 创建了一个包括51012张道路图像用于紧急识别和8928张图像用于长期跟踪的综合数据集.
- 使用构建的数据集训练和评估了六个成熟的对象检测算法.
- 该数据集有助于评估路面应急识别和跟踪算法性能.
主要成果:
- 新创建的数据集展示了六个对象检测算法的性能.
- 数据集的规模和多样性为人行道危险分析提供了增强的能力.
- 结果验证了数据集对训练和AI模型基准测试的有用性.
结论:
- 本研究介绍了第一个具有长期跟踪能力的大型路面应急数据集.
- 该数据集为动态监测和分析路面困境提供了必要的数据支持.
- 这些发现将有助于通过先进的AI应用优化道路维护策略.
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