Rail wear occurs continuously owing to the rolling contact load of trains and is fundamental for railway operational safety. A point-based manual rail wear inspection cannot satisfy the increasing demand for rapid, low-cost, and continuous monitoring. This paper proposes a depth-plus-region fusion network for detecting rail wear on a running band, which is a collection of wheel–rail interaction traces. The following steps are involved in the proposed method. (i) A depth map estimated by a modified MiDaS model is utilized as guidance for exploiting the depth information of the running band for rail wear detection. (ii) The running band of a rail is segmented and extracted from images using an improved mask region-based convolutional neural network that uses the scale and ratio information to perform instance segmentation of the running band images. (iii) A two-channel attention–fusion network that classifies rail wear is constructed. In this study, we collected real-world running band images and rail wear-related data to validate our approach using a high-accuracy rail-profile measurement tool. The case-study results demonstrated that the proposed method can rapidly and accurately detect rail wear under different ambient light conditions. Moreover, the recall rate of severe wear detection was 84.21%.

中文翻译：

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使用轨道运行带图像检测严重的轨道磨损

由于列车的滚动接触载荷，钢轨磨损会持续发生，这对铁路运营安全至关重要。基于点的人工轨道磨损检测不能满足日益增长的快速、低成本和连续监测的需求。本文提出了一种深度加区域融合网络，用于检测运行带上的钢轨磨损，该运行带是轮轨相互作用轨迹的集合。所提出的方法涉及以下步骤。(i) 由改进的 MiDaS 模型估计的深度图被用作利用运行带的深度信息进行轨道磨损检测的指导。(ii) 使用改进的基于掩模区域的卷积神经网络从图像中分割和提取轨道的运行带，该网络使用比例和比率信息来执行运行带图像的实例分割。(iii) 构建了一个对铁路磨损进行分类的双通道注意力融合网络。在这项研究中，我们收集了真实世界的运行带图像和与轨道磨损相关的数据，以使用高精度轨道轮廓测量工具验证我们的方法。案例研究结果表明，所提出的方法可以在不同环境光条件下快速准确地检测钢轨磨损。此外，严重磨损检测的召回率为84.21%。