Lin Shuai, Xu Cheng, Chen Lipei, Li Siqi, Tu Xiaohan
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
Sensors (Basel). 2020 Apr 14;20(8):2212. doi: 10.3390/s20082212.
High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.
高速铁路已成为全球最受欢迎的交通方式之一。作为高速铁路供电系统的重要组成部分，架空接触网系统（OCS）直接影响铁路的稳定运行，因此定期检查和维护至关重要。目前，人工检查效率低下且成本高昂，无法满足高速铁路运营的要求，自动检查成为一种趋势。点云中的三维信息对于接触网检查中的几何参数测量很有用。因此，从检查设备采集的点云数据中识别OCS的组件具有重要意义，这推动了参数测量的自动化。在本文中，我们提出了一种基于深度学习的新颖方法来识别OCS组件的点云。该方法通过卷积神经网络（CNN）识别每个单帧点云的上下文，并根据分类结果组合一些单帧数据，然后将它们输入到分割网络中以识别OCS组件。为了验证该方法，我们构建了一个包含八类的OCS组件点云数据集。实验结果表明，所提出的方法能够高精度地检测OCS组件。我们的工作可应用于实际的OCS组件检测，对OCS自动检查具有重要的现实意义。
