In the United States, to ensure railroad safety and keep its efficient operation, regular track inspections on track component defects are required by the Federal Railroad Administration (FRA). Various types of inspection equipment are applied, such as ground penetrating radar, laser, and LiDAR, but they are usually very expensive and require extensive training and rich experience to operate. To date, track inspections still rely heavily on manual inspections which are low‐efficiency, subjective, and not as accurate as desired, especially for missing and broken track components, such as spikes, clips, and tie plates. To address this issue, a real‐time pixel‐level rail components detection framework to inspect tracks timely and accurately is proposed in this study. The first public rail components image database, including rails, spikes, and clips, is built and released online. A real‐time pixel‐level detection framework with improved real‐time instance segmentation models is developed. The improved models leverage fast object detection and highly accurate instance segmentation. Backbones with more granular levels and receptive fields are implemented in the proposed models. Compared with the original YOLACT and Mask R‐CNN models, the proposed models are able to: (1) achieve 59.9 bbox mAP, and 63.6 mask mAP with the customized dataset, which are higher than the other models and (2) achieve a real‐time speed which is over 30 FPS processing a high‐resolution video (1,080 × 1,092) with a single GPU. The fast processing speed can quickly turn inspection videos into useful information to assist track maintenance. The railroad track components image dataset can be accessed at https://github.com/jonguo111/Rail_components_image_data

中文翻译：

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使用实时实例分割的自动铁轨组件检查

在美国，为了确保铁路安全并保持其有效运行，联邦铁路管理局（FRA）要求对轨道部件缺陷进行定期轨道检查。应用了各种类型的检查设备，例如探地雷达，激光和LiDAR，但它们通常非常昂贵，并且需要大量的培训和丰富的操作经验。迄今为止，轨道检查仍然严重依赖于效率低下，主观且不够理想的手动检查，尤其是对于缺少和损坏的轨道组件（例如尖钉，夹子和连接板）。为了解决这个问题，本研究提出了一种实时，像素级的轨道组件检测框架，可以及时，准确地检查轨道。第一个公共轨道组件图像数据库，包括轨道，尖峰和剪辑，是在线构建和发布的。开发了具有改进的实时实例分割模型的实时像素级检测框架。改进的模型利用了快速的对象检测和高度准确的实例分割功能。在建议的模型中实现了具有更细粒度级别和接受域的主干。与原始的YOLACT和Mask R‐CNN模型相比，所提出的模型能够：（1）通过自定义数据集实现59.9 bbox mAP和63.6 Mask MAP，高于其他模型，并且（2）实现真实模型。超过30 FPS的时间速度，使用单个GPU处理高分辨率视频（1,080×1,092）。快速的处理速度可以将检查视频快速转换为有用的信息，以帮助进行跟踪维护。可以在https：// github上访问铁轨组件图像数据集。