铁路轨道表面故障数据集数据集
Asfar Arain1, Sanaullah Mehran1, Muhammad Zakir Shaikh1,2
1NCRA MUET, NCRA Condition Monitoring Systems Lab, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan.
Data in brief
|February 1, 2024
概括
通过EKENH9R摄像头捕捉的铁路轨道表面缺陷的新数据集,有助于铁路维护和计算机视觉方面的研究. 本资源支持开发用于自动化轨道检查的机器学习 (ML) 和深度学习 (DL).
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 铁路基础设施的维护对于运输安全和效率至关重要.
- 轨道表面缺陷,如裂和裂纹,挑战了轨道的完整性.
- 现有研究需要全面的数据集来进行高级分析.
研究的目的:
- 介绍一套关于铁路轨道表面故障的新型数据集.
- 为铁路维护和计算机视觉研究提供有价值的资源.
- 促进ML / DL算法的开发,以检测缺陷.
主要方法:
- 用铁路检查车辆上的EKENH9R摄像头收集数据.
- 在各种条件下确保了多样化的现实世界故障表示.
- 为准确的分类提供了详细的注释和元数据.
主要成果:
- 现在可以获得各种铁路轨道表面缺陷的全面数据集.
- 数据集包括各种环境和照明条件下的图像.
- 附注允许精确的故障分类和严重程度评估.
结论:
- 该数据集是ML/DL和铁路维护中的图像处理的重要资产.
- 它支持自动检查和预测性维护系统的开发.
- 鼓励社区利用以推进轨道状况监测研究.
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