Railroad track inspections conducted in accordance with federal regulations and internal railway operating practices result in significant labor costs and occupy valuable network capacity. These factors, combined with advancements in the field of machine vision, have encouraged a transition from human visual inspections to machine-based alternatives. Commercial machine vision technologies for railway inspection currently exist, and automated analysis approaches—which deliver objective results—are available in some systems. However, they are limited to a “pass/fail” approach through the detection of components which fail to meet maintenance or geometry thresholds, as opposed to being able to detect subtle changes in track conditions to identify evolving problems. To overcome these limitations, this paper presents results from the field deployment and validation of a system that pairs three-dimensional (3D) machine vision with automated change detection technology. The change detection approach uses a deep convolution neural network (DCNN) to accurately characterize track conditions between repeat runs. Current automated track inspection technologies were studied, and the applicability of change detection is discussed. The paper presents the process for 3D image capture, DCNN training, and evaluation by comparing DCNN results to an expert human evaluator. Finally, it presents change detection results for fastener presence and spike height. Results indicate that this technology can successfully identify fasteners and spikes with percent accuracies greater than 98% and that it can successfully generate change detection results for comparison of track condition among runs.

中文翻译：

---

使用深度卷积神经网络和变化检测技术进行铁路轨道检查

根据联邦法规和内部铁路运营实践进行的铁路轨道检查会导致大量劳动力成本并占用宝贵的网络容量。这些因素，再加上机器视觉领域的进步，促使人们从人类视觉检查过渡到基于机器的替代方案。目前存在用于铁路检查的商用机器视觉技术，并且在某些系统中可以使用提供客观结果的自动化分析方法。但是，它们仅限于“通过/失败”方法，通过检测未能满足维护或几何阈值的组件，而不是能够检测轨道条件的细微变化以识别不断发展的问题。为了克服这些限制，本文介绍了将三维 (3D) 机器视觉与自动变化检测技术配对的系统的现场部署和验证结果。变化检测方法使用深度卷积神经网络 (DCNN) 来准确表征重复运行之间的轨道条件。研究了当前的自动轨道检测技术，并讨论了变化检测的适用性。本文通过将 DCNN 结果与专家人类评估者进行比较，介绍了 3D 图像捕获、DCNN 训练和评估的过程。最后，给出了紧固件存在和尖峰高度的变化检测结果。