School of Software Engineering, BeijingJiaotong University, Beijing, 100044, China.
School of Physical Science and Engineering, BeijingJiaotong University, Beijing, 100044, China.
Sci Data. 2024 May 6;11(1):462. doi: 10.1038/s41597-024-03288-y.
Railway transportation has experienced significant growth worldwide, offering numerous benefits to society. Most railway accidents are caused by wheelset faults so it's significant to monitor wheelset conditions. Therefore, we need to collect wheelset images, repaint them, extract laser stripe centerlines, construct 3D contour, and measure their geometric parameters to judge the wheelset's conditions. Deep learning can fulfill the tasks satisfyingly because it's adaptable, robust, and generalize compared with traditional methods. To train the deep learning models effectively, we need rich and high-quality wheelset datasets. So far, there are no applicable public train wheelset datasets available, which greatly hinders the research on train wheelsets. Thus we construct a publicly available Wheelset Laser Image Dataset (WLI-Set). WLI-Set consists of four sub-datasets, Original, Inpainting, Segmentation, and Centerline. The dataset contains abundant annotated multiline laser stripe images that can facilitate the research on train wheelsets effectively.
铁路运输在全球范围内得到了迅猛发展，为社会带来了诸多益处。大多数铁路事故是由轮对故障引起的，因此监测轮对状态至关重要。为此，我们需要采集轮对图像，对其进行重新绘制，提取激光条纹中心线，构建 3D 轮廓，并测量其几何参数，以判断轮对的状态。与传统方法相比，深度学习具有适应性强、鲁棒性好、泛化能力强等优点，可以很好地完成这些任务。为了有效地训练深度学习模型，我们需要丰富且高质量的轮对数据集。到目前为止，还没有适用的公共火车轮对数据集，这极大地阻碍了火车轮对的研究。因此，我们构建了一个公开可用的轮对激光图像数据集（WLI-Set）。WLI-Set 包含四个子数据集，分别是 Original、Inpainting、Segmentation 和 Centerline。该数据集包含丰富的标注多线激光条纹图像，可有效促进火车轮对的研究。
