PY-CrackDB:来自巴拉圭道路的路面裂数据集,用于上下文感知计算机视觉模型
Fredy Gabriel Ramírez-Villanueva1,2, José Luis Vázquez Noguera1, Horacio Legal-Ayala1
1Facultad Politécnica, Universidad Nacional de Asunción, San Lorenzo, Paraguay.
Data in brief
|October 10, 2025
概括
一个新的青路面图像数据集,PY-CrackDB,有助于AI开发用于早期道路缺陷检测. 它专注于微小的裂,对于预防性维护策略至关重要.
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
- 数据科学数据科学数据科学
背景情况:
- 道路维护依赖于准确的缺陷检测.
- 早期识别路面损伤,特别是细小的裂,对于成本有效的维修至关重要.
- 现有的数据集可能无法充分满足在青路面上早期发现裂的特定需求.
研究的目的:
- 介绍PY-CrackDB,一个新的数据集的青路面图像.
- 为开发用于道路维护的上下文感知人工智能系统提供资源.
- 特别支持用于早期干预的细裂 (宽度<3毫米) 的检测.
主要方法:
- 收集了来自巴拉圭国家公路的569张青路面图像.
- 有注释的图像显示存在或不存在裂,重点关注微小的裂.
- 通过由土木工程专业人员进行标准化收集和验证来确保数据质量.
主要成果:
- PY-CrackDB包含369个有裂的图像和200个没有裂的图像.
- 该数据集适合用于训练AI模型进行语义细分和早期缺陷检测.
- 注释经过严格的交叉审查以确保准确性.
结论:
- PY-CrackDB是推动道路维护人工智能的宝贵资源.
- 该数据集通过改进的裂检测,促进了预防性维护策略的制定.
- 专注于细小的裂,可以更早,更有效地修复道路.
关键词:
青路面是一种青路面.裂纹检测 裂纹检测 裂纹检测 裂纹检测深度学习是一种深度学习.图像细分 图像细分 图像细分对象检测检测对象检测对象检测巴拉圭的基础设施设施.道路维护 道路维护 道路维护 道路维护更多相关视频
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