Deep Neural Networks for Railway Switch Detection and Classification Using Onboard Camera Images

Jahan, Kanwal; Niemeijer, Joshua; Kornfeld, Nils; Roth, Michael

doi:10.1109/ssci50451.2021.9659983

Cited by 10 publications

(5 citation statements)

References 18 publications

(38 reference statements)

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“…The CNN is mainly designed for image data [ 81 , 82 , 83 ], and the RNN is widely used to analyse time series and sequence data [ 23 , 84 ]. The DNN can be used for track component classification [ 85 ], railway defect detection [ 86 ], and track settlement prediction [ 87 ]. The ANN is commonly used for engineering to explore the importance of input features to output parameters.…”

Section: Prediction Methods Based On Machine Learningmentioning

confidence: 99%

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

Liao

Liu

Wang³

et al. 2022

Sensors

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Keeping railway tracks in good operational condition is one of the most important tasks for railway owners. As a result, railway companies have to conduct track inspections periodically, which is costly and time-consuming. Due to the rapid development in computer science, many prediction models using machine learning methods have been developed. It is possible to discover the degradation pattern and develop accurate prediction models. The paper reviews the existing prediction methods for railway track degradation, including traditional methods and prediction methods based on machine learning methods, including probabilistic methods, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Grey Model (GM). The advantages, shortage, and applicability of methods are discussed, and recommendations for further research are provided.

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Section: Prediction Methods Based On Machine Learningmentioning

confidence: 99%

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

Liao

Liu

Wang³

et al. 2022

Sensors

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“…In another study, accurate estimation of recoverable train delay can assist railway dispatchers in rescheduling trains and improving service reliability [21]. The researchers aimed to develop a model for predicting primary delay recovery (PDR) based on operational data from the Wuhan-Guangzhou (W-G) high-speed railway.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Although the model's foundation was built using W-G HSR data, it could be easily extended to other HSR systems. The proposed approach could benefit professionals and academics interested in disruption management [21].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Neural Network-Directed Detection and Localization of Faults in Railway Track Circuits: An Application of Dempster-Shafer Theory

Mhmood,

Nasret,

Noori

et al. 2023

ISI

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Track circuits, integral to the security infrastructure of railway traffic systems, govern the operational status of train lines. The swift detection and rectification of faults within these circuits is critical to preserve the integrity and functionality of rail networks. In this study, an innovative approach, leveraging neural networks in tandem with Dempster-Shafer theory, is proposed for detecting and localizing faults in track circuits. The complexities of fault detection are deconstructed into more manageable, capacitor-specific pattern recognition challenges, with the resolutions amalgamated via Dempster-Shafer theory. Simulations demonstrate the efficacy of this method, yielding a detection accuracy exceeding 98% and a localization accuracy surpassing 93%. This marks a significant improvement over contemporary reference techniques, thereby setting a new benchmark in the domain of track circuit fault detection and localization.

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“…DNN models can also be used for track component classification, e.g. railway switch type classification [45]. In addition, Shibao and Jie [46] applied the DNN model to landslide susceptibility mapping of the Sichuan-Tibet Railway and found that the DNN model can provide an accuracy of 84% in both the training and testing process.…”

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of railway track longitudinal level using multiple machine learning methods

Han,

Liao,

Wang

et al. 2023

Meas. Sci. Technol.

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With the fast development of computer science, many prediction models based on machine learning methods have been used in the railway industry, which can better predict the random characteristics in track degradation, plan maintenance activities, and eventually meet the requirement of railway transportation. However, the applicability and generality of these models are unclear and the comparative analysis of these models on the random railway track is rare, which makes it hard for railway engineers to choose the most suitable prediction models in practice. In this paper, the track longitudinal level of a section of a railway which measured monthly by Rail Infrastructure Alignment Acquisition system (RILA) for 1.5 years in the Netherlands has been analysed using multiple mathematical methods. After that, three machine learning-based prediction models were developed to predict the future development of the track longitudinal level, using Support Vector Machine (SVM), Grey Model (GM) and Deep Neural Network (DNN). The prediction performance of different prediction models is compared and discussed. Recommendations for choosing prediction models and further development are provided.

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Deep Neural Networks for Railway Switch Detection and Classification Using Onboard Camera Images

Cited by 10 publications

References 18 publications

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

Neural Network-Directed Detection and Localization of Faults in Railway Track Circuits: An Application of Dempster-Shafer Theory

Analysis and prediction of railway track longitudinal level using multiple machine learning methods

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