ConcreteCARB:用于计算机视觉任务的混凝土碳化综合图像数据集
José A Guzmán-Torres1, Sandra Del C Arguello-Hernández1, Francisco J Domínguez-Mota1
1Civil Engineering Faculty, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán, 58030, Mexico.
Data in brief
|February 9, 2026
概括
混凝土CARB数据集提供了903张图像,用于人工智能驱动的混凝土碳化检测. 该资源有助于通过机器学习进行结构健康监测和混凝土耐久性研究.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 由于碳化而导致的混凝土退化对结构完整性构成重大风险.
- 准确和早期检测混凝土碳化对于有效的结构健康监测至关重要.
- 现有的碳化评估方法可能是劳动密集型和主观的.
研究的目的:
- 介绍ConcreteCARB数据集,这是基于人工智能的混凝土碳化检测的新型资源.
- 支持用于具体诊断的机器学习和计算机视觉模型的开发和基准测试.
- 促进自动化结构健康监测和材料降解分析方面的研究.
主要方法:
- 收集了903张混凝土表面的高分辨率图像,这些图像在180天内被自然碳化.
- 应用甲烯测试以揭示碳化前线,然后由专家工程师手动分割和视觉标记.
- 包括各种混合设计,水-水泥比率和添加剂 (工业废物,Opuntia ficus-indica) 的混凝土样本.
主要成果:
- 对于二元分类任务,ConcreteCARB数据集被系统地组织成"碳化样本"和"无碳化存在".
- 该数据集提供了可靠的基础真相数据,对于培训和验证AI模型至关重要.
- 图像是在三星SM-S901U1智能手机上使用一致的设置捕获的.
结论:
- ConcreteCARB数据集是土木工程和人工智能领域的研究人员和工程师的宝贵,公开可访问的资源.
- 它可以开发用于混凝土碳化损伤的自动检测,分类和细分模型.
- 该数据集促进了可重复性,并在具体的耐用性和可持续性研究中推进了人工智能应用.
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