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Evaluating Railroad Ballast Degradation Trends Using Machine Vision and Machine Learning Techniques

机译：使用机器视觉和机器学习技术评估铁路道Ball的退化趋势

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摘要

Recently, automatic ballast sampling (ABS) methods have been introduced to the railroad industry to obtain a sample of ballast and underlying layers. Currently, manual-visual classification methods are used by experts to identify fouling conditions and degradation trends in the collected ballast samples. This paper presents an innovative approach developed for objective classification of ballast degradation using the combination of machine vision and machine learning techniques. Initially, various computer vision algorithms were used to generate features associated with images of ballast cross sections at different degradation levels. Next, the generated features were used alongside a visual classification database provided by experts to develop, train, validate, and test a feed forward artificial neural network (ANN) using a supervised learning method. This work was further extended by implementing convolutional neural networks (CNNs) to serve as automatic feature generators. The findings of this study showed that the proposed CNNs with an optimized topology could successfully classify ballast fouling in an effective and repeatable fashion with reasonable error levels. Further improvement of this technology holds the potential to provide a tool for consistent and automated ballast inspection and life cycle analysis intended to improve the safety and network reliability of US railroad transportation system.
机译：最近，自动压载物采样（ABS）方法已被引入铁路行业，以获取压载物和底层的样品。当前，专家使用手动视觉分类方法来识别收集的压载样品中的结垢状况和降解趋势。本文提出了一种创新的方法，该方法结合了机器视觉和机器学习技术，用于镇流器退化的客观分类。最初，各种计算机视觉算法被用来生成与不同降解水平的压载物横截面图像相关的特征。接下来，将生成的特征与专家提供的视觉分类数据库一起使用，以使用监督学习方法开发，训练，验证和测试前馈人工神经网络（ANN）。通过实现卷积神经网络（CNN）作为自动特征生成器，可以进一步扩展这项工作。这项研究的结果表明，所提出的具有优化拓扑的CNN可以有效且可重复地以合理的误差水平成功地对压载物结垢进行分类。这项技术的进一步改进为提供稳定，自动化的压载物检查和生命周期分析的工具提供了潜力，该工具旨在提高美国铁路运输系统的安全性和网络可靠性。

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