Chen Lipei, Xu Cheng, Lin Shuai, Li Siqi, Tu Xiaohan
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
Sensors (Basel). 2020 Apr 15;20(8):2224. doi: 10.3390/s20082224.
The overhead contact system (OCS) is a critical railway infrastructure for train power supply. Periodic inspections, aiming at acquiring the operational condition of the OCS and detecting problems, are necessary to guarantee the safety of railway operations. One of the OCS inspection means is to analyze data of point clouds collected by mobile 2D LiDAR. Recognizing OCS components from the collected point clouds is a critical task of the data analysis. However, the complex composition of OCS makes the task difficult. To solve the problem of recognizing multiple OCS components, we propose a new deep learning-based method to conduct semantic segmentation on the point cloud collected by mobile 2D LiDAR. Both online data processing and batch data processing are supported because our method is designed to classify points into meaningful categories of objects scan line by scan line. Local features are important for the success of point cloud semantic segmentation. Thus, we design an iterative point partitioning algorithm and a module named as Spatial Fusion Network, which are two critical components of our method for multi-scale local feature extraction. We evaluate our method on point clouds where sixteen categories of common OCS components have been manually labeled. Experimental results show that our method is effective in multiple object recognition since mean Intersection-over-Unions (mIoUs) of online data processing and batch data processing are, respectively, 96.12% and 97.17%.
架空接触网系统（OCS）是铁路列车供电的关键基础设施。定期检查对于保证铁路运营安全至关重要，其目的是获取OCS的运行状况并检测问题。OCS检查手段之一是分析由移动二维激光雷达收集的点云数据。从收集到的点云中识别OCS组件是数据分析的一项关键任务。然而，OCS的复杂组成使得这项任务颇具难度。为了解决识别多个OCS组件的问题，我们提出一种基于深度学习的新方法，对移动二维激光雷达收集的点云进行语义分割。由于我们的方法旨在逐扫描线将点分类为有意义的对象类别，因此支持在线数据处理和批数据处理。局部特征对于点云语义分割的成功至关重要。因此，我们设计了一种迭代点划分算法和一个名为空间融合网络的模块，它们是我们用于多尺度局部特征提取方法的两个关键组件。我们在已手动标注了16类常见OCS组件的点云上评估我们的方法。实验结果表明，我们的方法在多目标识别方面是有效的，因为在线数据处理和批数据处理的平均交并比（mIoU）分别为96.12%和97.17%。
