According to the Federal Railroad Administration (FRA) database, track component failure is one of the major factors causing train accidents. To improve railroad safety and reduce accident occurrence, tracks need to be regularly inspected. Many computer-aided track inspection methods have been introduced over the past decades, however, inspecting missing or broken track components still heavily relies on manual inspections. To address those issues, this study proposes a real-time and cost-effective computer vision-based framework to inspect track components quickly and efficiently. The cutting-edge convolutional neural network, YOLOv4 is improved trained, and evaluated based on the images in a public track components image database. Compared with other one-stage object detection models, the customized YOLOv4-hybrid model can achieve 94.4 mean average precision (mAP) and 78.7 frames per second (FPS), which outperforms other models in terms of both accuracy and processing speed. It paves the way for developing portable and high-speed track inspection tools to reduce track inspection cost and improve track safety.

根据联邦铁路管理局（FRA）数据库，轨道组件故障是导致火车事故的主要因素之一。为了提高铁路安全性并减少事故的发生，需要定期检查轨道。在过去的几十年中，已经引入了许多计算机辅助的轨道检查方法，但是，检查丢失或损坏的轨道组件仍然严重依赖于手动检查。为了解决这些问题，本研究提出了一种实时且经济高效的基于计算机视觉的框架，以快速有效地检查跟踪组件。先进的卷积神经网络YOLOv4经过改进的训练，并根据公共轨道组件图像数据库中的图像进行评估。与其他单阶段目标检测模型相比，定制的YOLOv4混合模型可以达到94。4个平均平均精度（mAP）和每秒78.7帧（FPS），在准确性和处理速度方面均优于其他模型。它为开发便携式和高速轨道检查工具铺平了道路，从而降低了轨道检查成本并提高了轨道安全性。