LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection.

As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.

A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR.

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%.

LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning.

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.

[Grape seed proanthocyanidin extracts inhibit lipopolysaccharide of Porphyromonas gingivalis].

PURPOSE: To study the inhibitory effect of grape seed proanthocyanidin extracts on 1ipopolysacharides of Porphyromonas gingivalis. METHODS: Grape seed proanthocyanidin extracts were double-diluted to different concentrations by two-fold dilution. The effect on Porphyromonas gingivalis was measured by minimal inhibitory concentration (MIC). The inhibition effect of grape seed proanthocyanidin extracts on lipopolysaccharide was measured by limulus assay with 6 concentrations below the minimal inhibitory concentration. SPSS 17.0 software package was used for statistical analysis. RESULTS: The value of MIC of grape seed proanthocyanidin extracts was 0.8 mg/mL to the strains of the experimental bacteria. The inhibitory effect on lipopolysaccharide enhanced with the increasing of the concentration of grape seed proanthocyanidin extracts within the scope of 0.05 to 0.4 mg/mL (P<0.05,0.01). CONCLUSIONS: Grape seed proanthocyanidin extracts have inhibitory effect on lipopolysaccharide of Porphyromonas gingivalis.

[Clinical value of three-dimensional dynamic contrast-enhanced MR angiography in diagnosis of angiostenosis after liver transplantation].

OBJECTIVE: To evaluate the clinical application of three-dimensional dynamic contrast-enhanced MR angiography (3D DCE MRA) in diagnosis of angiostenosis after liver transplantation. METHODS: Twenty recipients of liver transplantation underwent 3D DCE MRA examination. The blood vessel rating grades were accessed and the relative diameter of vascular anastomosis was measured; and the results were compared with those of US or DSA examination. RESULTS: Satisfactory angiography images were obtained in all cases by 3D DCE MRA, including 11 cases with normal and mild stenosis, 5 with moderate and 4 with severe stenosis in hepatic artery. Except one case in which 3D DCE MRA showed severe stenosis but DSA showed moderate stenosis, the results of MRA were all consistent with those of US or/and DSA in the stenosis degree of the portal vein, hepatic vein and the postcava. CONCLUSION: 3D DCE MRA is an effective technique to evaluate the degree of angiostenosis after liver transplantation.