Detection of Rail Defects with Deep Learning Controlled Autonomous UAV

Aydın, İlhan; Sevi, Mehmet; Şahbaz, Kadir; Karaköse, Mehmet

doi:10.1109/icdabi53623.2021.9655796

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…The deterioration of the railway line, the wear of the materials on the lines over time, foreign materials falling on the railway, and deliberate sabotage on the railway put the safety of railway passengers at risk. Considering all these factors, a fast and efficient inspection system is required to ensure the safety of railways [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Railway Security System Design by Image Processing and Deep Learning Unmanned Aerial Vehicle

EYLENCE

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Özmen

et al. 2022

Türk Doğa Ve Fen Dergisi

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With the developing technology, technological blessings make human life easier and help them every day. Unmanned aerial vehicles (UAV), which is one of the technological blessings, have shown themselves in many fields, especially in fields such as the military, defense industry, photography, and hobby. With the development of defense systems with UAVs, the security of railways has also been left to UAVs. In this study, while the foreign matter separation is made on the railway by using the deep learning model in real-time, the image taken on the UAV is simultaneously controlled by using the image processing method. The fact that the deep learning model has a 0.99 mAP rate increases the reliability of the model.

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Section: Introductionmentioning

confidence: 99%

Railway Security System Design by Image Processing and Deep Learning Unmanned Aerial Vehicle

EYLENCE

YÜCEL>

Özmen

et al. 2022

Türk Doğa Ve Fen Dergisi

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A study on the application of convolutional neural networks for the maintenance of railway tracks

Pappaterra,

Flammini

2024

Discov Artif Intell

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This paper provides an overview of the applications of Convolutional Neural Networks (CNN) in the railway maintenance industry. Our research covers specifically the subdomain of railway track maintenance. In this study, we have analyzed the state-of-the-art of CNNs applied to railway track maintenance by conducting an extensive literature review, summarizing different tasks and problems related to the topic and presenting solutions based on CNNs with a special emphasis on the data used to create these models. The results of our research show different applications of CNNs within the scope, including the detection of defects in the surface of railway rails and railway track components, such as fasteners, joints, sleepers, switches and crossings, as well as the recognition of track components, and the continuous monitoring of railway tracks. The architecture of CNNs is fitting to learning spatial hierarchies of features directly from the input data, making them of great use for Computer Vision and other applications related to the topic at hand. The implementation of IoT devices and smart sensors aid the collection of real-time data which can be used to feed powerful CNN models to recognize patterns and identify complex events related to the maintenance of railway tracks. This and more insights are discussed in detail within the contents of this paper.

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Multiclass Classification of Rail Track Defects Using Deep Learning Techniques

Bhat,

Karegowda,

2023

2023 International Conference on Smart Systems for Applications in Electrical Sciences (ICSSES)

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Detection of Rail Defects with Deep Learning Controlled Autonomous UAV

Cited by 4 publications

References 18 publications

Railway Security System Design by Image Processing and Deep Learning Unmanned Aerial Vehicle

Railway Security System Design by Image Processing and Deep Learning Unmanned Aerial Vehicle

A study on the application of convolutional neural networks for the maintenance of railway tracks

Multiclass Classification of Rail Track Defects Using Deep Learning Techniques

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