TurnoutSegmentation: A Semantic Segmentation Dataset for Railway Single Turnouts

The TurnoutSegmentation dataset is the first publicly available dataset for fine-grained visual analysis of railway switch equipment, providing key data support to address bottlenecks such as the lack of visualization of mechanical structural deformation and insufficient modeling of electrical mechanical state associations in traditional detection. Its core value lies in providing standardized pixel level semantic labels for components (sharp rails, switch machines, basic rails, and close fitting adjustment rods), directly serving intelligent operation and maintenance scenarios: achieving component gap quantification through precise segmentation, supporting cross domain analysis such as track state evaluation and predictive maintenance. The data aggregation is designed as a ready to use benchmark, consisting of 2360 high-definition images and accompanying annotations, divided into a training set (1610 frames), a validation set (691 frames), and a test set (59 frames). Users can use component masks to develop defect detection algorithms, or combine position coordinates to achieve deformation displacement analysis.

道岔分割（TurnoutSegmentation）数据集是首个公开可用的铁路道岔设备细粒度视觉分析专用数据集，可为解决传统检测中机械结构变形可视化缺失、机电状态关联建模不足等核心瓶颈提供关键数据支撑。该数据集的核心价值在于为道岔核心部件（尖轨、转辙机、基本轨及密贴调整杆）提供标准化像素级语义标注，可直接赋能智能运维场景：通过精准的像素分割实现部件间隙量化分析，支撑轨道状态评估、预测性维护等跨领域研究与应用。该数据集整体被设计为即用型基准数据集，共包含2360张高清图像及配套标注文件，划分为训练集（1610帧）、验证集（691帧）与测试集（59帧）。开发者可借助部件掩码（component masks）开发缺陷检测算法，或结合部件位置坐标实现变形位移分析。

搜集汇总

背景与挑战

背景概述

TurnoutSegmentation是首个公开的铁路道岔语义分割数据集，包含2360张高清图像及像素级标注，覆盖尖轨、转辙机等关键部件。该数据集旨在支持智能运维，通过精确分割实现部件间隙量化和变形分析，适用于缺陷检测与预测性维护等任务。