Estimation of railway track longitudinal irregularity using vehicle response with information compression and Bayesian deep learning

2021 ◽  
Author(s):  
Chenzhong Li ◽  
Qing He ◽  
Ping Wang
Keyword(s):  
Deep Learning ◽  
Railway Track ◽  
Information Compression ◽  
Vehicle Response

Railway Track Fasteners Fault Detection using Deep Learning

2019 ◽  
Author(s):  
Ya-Wen Lin ◽  
Chen-Chiung Hsieh ◽  
Wei-Hsin Huang ◽  
Sun-Lin Hsieh ◽  
Wei-Hung Hung
Keyword(s):  
Deep Learning ◽  
Fault Detection ◽  
Railway Track

scholarly journals RAPID OBJECT DETECTION SYSTEMS, UTILISING DEEP LEARNING AND UNMANNED AERIAL SYSTEMS (UAS) FOR CIVIL ENGINEERING APPLICATIONS

2018 ◽  
Vol XLII-2 ◽  
pp. 391-398 ◽  
Author(s):  
D. Griffiths ◽  
J. Boehm
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Network Architecture ◽  
Model Performance ◽  
Image Understanding ◽  
Detection Algorithm ◽  
Railway Track ◽  
Track Length ◽  
Unmanned Aerial Systems ◽  
Learning Approaches

With deep learning approaches now out-performing traditional image processing techniques for image understanding, this paper accesses the potential of rapid generation of Convolutional Neural Networks (CNNs) for applied engineering purposes. Three CNNs are trained on 275 UAS-derived and freely available online images for object detection of 3m2 segments of railway track. These includes two models based on the Faster RCNN object detection algorithm (Resnet and Incpetion-Resnet) as well as the novel onestage Focal Loss network architecture (Retinanet). Model performance was assessed with respect to three accuracy metrics. The first two consisted of Intersection over Union (IoU) with thresholds 0.5 and 0.1. The last assesses accuracy based on the proportion of track covered by object detection proposals against total track length. In under six hours of training (and two hours of manual labelling) the models detected 91.3 %, 83.1 % and 75.6 % of track in the 500 test images acquired from the UAS survey Retinanet, Resnet and Inception-Resnet respectively. We then discuss the potential for such applications of such systems within the engineering field for a range of scenarios.

scholarly journals Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4325
Author(s):  
Tiange Wang ◽  
Fangfang Yang ◽  
Kwok-Leung Tsui
Keyword(s):  
Deep Learning ◽  
Learning Technologies ◽  
Railway Track ◽  
Detection Accuracy ◽  
Learning Approaches ◽  
Learning Networks ◽  
Railway Transportation ◽  
One Stage ◽  
Input Size ◽  
Feature Pyramid

Railway inspection has always been a critical task to guarantee the safety of the railway transportation. The development of deep learning technologies brings new breakthroughs in the accuracy and speed of image-based railway inspection application. In this work, a series of one-stage deep learning approaches, which are fast and accurate at the same time, are proposed to inspect the key components of railway track including rail, bolt, and clip. The inspection results show that the enhanced model, the second version of you only look once (YOLOv2), presents the best component detection performance with 93% mean average precision (mAP) at 35 image per second (IPS), whereas the feature pyramid network (FPN) based model provides a smaller mAP and much longer inference time. Besides, the detection performances of more deep learning approaches are evaluated under varying input sizes, where larger input size usually improves the detection accuracy but results in a longer inference time. Overall, the YOLO series models could achieve faster speed under the same detection accuracy.

scholarly journals Application of Deep Learning Networks to Segmentation of Surface of Railway Tracks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4065
Author(s):  
Piotr Bojarczak ◽  
Waldemar Nowakowski
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Vision System ◽  
Gaussian Function ◽  
Railway Track ◽  
Learning Networks ◽  
Improve Performance ◽  
Speed Up ◽  
Visual Geometry

The article presents a vision system for detecting elements of railway track. Four types of fasteners, wooden and concrete sleepers, rails, and turnouts can be recognized by our system. In addition, it is possible to determine the degree of sleeper ballast coverage. Our system is also able to work when the track is moderately covered by snow. We used a Fully Convolutional Neural Network with 8 times upsampling (FCN-8) to detect railway track elements. In order to speed up training and improve performance of the model, a pre-trained deep convolutional neural network developed by Oxford’s Visual Geometry Group (VGG16) was used as a framework for our system. We also verified the invariance of our system to changes in brightness. To do this, we artificially varied the brightness of images. We performed two types of tests. In the first test, we changed the brightness by a constant value for the whole analyzed image. In the second test, we changed the brightness according to a predefined distribution corresponding to Gaussian function.

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.

Deep Learning Based Object and Railway Track Recognition Using Train Mounted Thermal Imaging System

2020 ◽  
Vol 17 (11) ◽  
pp. 5062-5071
Author(s):  
Rajiv Kapoor ◽  
Rohini Goel ◽  
Avinash Sharma
Keyword(s):  
Deep Learning ◽  
Thermal Imaging ◽  
Imaging System ◽  
Night Vision ◽  
Railway Track ◽  
Validation Data ◽  
Ir Camera ◽  
Railway Tracks ◽  
Railway Safety ◽  
Thermal Imaging System

An intelligent railways safety system is very essential to avoid the accidents. The motivation behind the problem is the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. Continuous research is being carried out by distinct researchers to ensure railway safety and to reduce accident rates. In this paper, a novel method is proposed for identifying objects (obstacles) on the railway tracks in front of a moving train using a thermal camera. This approach presents a novel way of detecting the railway tracks as well as a deep network based method to recognize obstacles on the track. A pre-trained network is used that provides the model understanding of real world objects and enables deep learning classifiers for obstacle identification. The validation data is acquired by thermal imaging using night vision IR camera. In this work, the Faster R-CNN is used that efficiently recognize obstacles on the railway tracks. This process can be a great help for railways to reduce accidents and financial burdens. The result shows that the proposed method has good accuracy of approximately 83% which helps to enhance the railway safety.

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