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深度学习技术用于从超声波数据中检测轨道指示,用于自动化轨道监控和维护
Md Ashraful Islam1, Georg Olm1
1Chair of Civil Systems Engineering, Technical University of Berlin, Gustav-Meyer-Allee 25, 13355 Berlin, Germany.
Ultrasonics
|April 16, 2024
概括
深度学习模型通过超声波图像准确地检测铁路缺陷,改善铁路维护. 这种自动化方法提高了铁路监控的安全性和效率.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 越来越多的铁路使用加速了磨损和表面缺陷,需要有效的识别方法.
- 传统的铁路缺陷视觉检查是劳动密集型的,容易出错,缺乏准确性.
- 深度学习为加强铁路维护和监控系统提供了潜力.
研究的目的:
- 使用超声波图像数据开发和比较用于检测铁路文物和缺陷的深度学习模型.
- 评估图像分类和物体检测的实用性,以从超声波数据中识别轨道指示.
- 确定用于自动化轨道缺陷识别的高级深度学习模型.
主要方法:
- 超声波轨道图像的数据预处理和标签.
- 使用常规神经网络 (CNN) 开发图像分类模型.
- 实施物体检测模型,特别是YOLOv5,用于缺陷定位.
主要成果:
- 卷积神经网络 (CNN) 在铁路缺陷的图像分类方面实现了98%的准确性.
- YOLOv5物体检测模型在识别铁路文物方面表现出99%的准确性.
- 这两种模型都在现实场景中被证明是有效的,用于准确有效地检测缺陷.
结论:
- 深度学习模型,特别是YOLOv5,对于从超声波数据中自动识别铁路缺陷非常有效.
- 这些先进的模型显著提高了铁路基础设施监控的准确性和效率.
- 开发的模型通过增强的维护实践,有助于使铁路运行更安全,更有效.
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