Deep learning-based visual ensemble method for high-speed railway catenary clevis fracture detection

2020 ◽  
Vol 396 ◽  
pp. 556-568 ◽  
Author(s):  
Ye Han ◽  
Zhigang Liu ◽  
Yang Lyu ◽  
Kai Liu ◽  
Changjiang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Ensemble Method ◽  
Fracture Detection ◽  
High Speed Railway

A deep learning forecasting method for frost heave deformation of high-speed railway subgrade

2021 ◽  
pp. 103265
Author(s):  
Jing Chen ◽  
Anyuan Li ◽  
Chunyan Bao ◽  
Yanhua Dai ◽  
Minghao Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
Frost Heave ◽  
High Speed Railway ◽  
Forecasting Method

Adaptive Deep Learning for High-Speed Railway Catenary Swivel Clevis Defects Detection

2020 ◽  
pp. 1-12
Author(s):  
Shibin Gao ◽  
Gaoqiang Kang ◽  
Long Yu ◽  
Dongkai Zhang ◽  
Xiaoguang Wei ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Speed ◽  
High Speed Railway

scholarly journals Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 413
Author(s):  
Shulun Wang ◽  
Feng Liu ◽  
Bin Liu
Keyword(s):  
Deep Learning ◽  
Optical Sensors ◽  
High Speed ◽  
Railway Track ◽  
Detection Methods ◽  
High Speed Railway ◽  
Safety Risks ◽  
Data Driven Approach ◽  
Distributed Optical Fiber ◽  
Selection Of

High deployment costs, safety risks, and time delays restrict traditional track detection methods in high-speed railways. Therefore, approaches based on optical sensors have become the most remarkable strategy in terms of deployment cost and real-time performance. Owing to the large amount of data obtained by sensors, it has been proven that deep learning, as a powerful data-driven approach, can perform effectively in the field of track detection. However, it is difficult and expensive to obtain labeled data from railways during operation. In this study, we used a segment of a high-speed railway track as the experimental object and deployed a distributed optical fiber acoustic system (DAS). We propose a track detection method that innovatively leverages semi-supervised deep learning based on image recognition, with a particular pre-processing for the dataset and a greedy algorithm for the selection of hyper-parameters. The superiority of the method was verified in both experiments and actual applications.

scholarly journals LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2212 ◽  
Author(s):  
Shuai Lin ◽  
Cheng Xu ◽  
Lipei Chen ◽  
Siqi Li ◽  
Xiaohan Tu
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
High Speed ◽  
Point Clouds ◽  
Parameter Measurement ◽  
Stable Operation ◽  
Automatic Inspection ◽  
High Speed Railway ◽  
Single Frame ◽  
Catenary System

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.

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