Field Imaging Based Assessment of In-Service Ballast Condition Authors
Field Imaging Based Assessment of In-Service Ballast Condition Authors
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发表时间：
2016
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通讯作者：
E. Tutumluer;N. M. Urbana;M. Moaveni;J. Hart;Mike McHenry
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作者：
E. Tutumluer;N. M. Urbana;M. Moaveni;J. Hart;Mike McHenry
道碴的降解会导致排水不良、沉降和横向稳定性降低，从而对铁路轨道的安全和性能产生不利影响。北美铁路行业通常使用两个指数来量化道碴退化的水平。它们是（i）污垢指数（FI）和（ii）污垢百分比（PF）。识别这两个传统的指标需要在实验室中进行压载取样和机械筛分析，这可能是耗时、费力和昂贵的。另外，沿着轨道沿着收集道渣样本的位置是局部的，并且与任何其他采样方法一样，可能不能正确地表示整个道渣横截面。作为一种自动化的替代方案，基于机器视觉的检测系统有可能从现场捕获的道碴层的高分辨率图像中直接客观地评估道碴状况和退化水平。本文介绍了一个创新的计算机视觉方法的初步开发和实施，用于评估道碴横截面的退化水平。先进的图像增强方法，
Ballast degradation can cause poor drainage, settlement and reduced lateral stability, which adversely affect railroad track safety and performance. Two indices are commonly used in the North American railroad industry to quantify the level of ballast degradation. These are (i) Fouling Index (FI) and (ii) Percentage Fouling (PF). Identifying these two traditional indices involve ballast sampling and mechanical sieve analyses in the laboratory, which can be time consuming, laborious and costly. Additionally, the locations along the track where ballast samples are collected are localized and like in any other sampling methods may not properly represent the entire ballast cross section. As an automated alternative, machine-vision-based inspection systems have the potential to directly and objectively assess ballast condition and degradation levels from high resolution images of ballast layers captured in the field. This paper presents the initial development and implementation of an innovative computer vison approach for assessing the degradation levels of ballast cross sections. Advanced image enhancement methods,