<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Rail track breakages represent broken structures consisting of rail track on the railroad. The traditional methods for detecting this problem have proven unproductive. The safe operation of rail transportation needs to be frequently monitored because of the level of trust people have in it and to ensure adequate maintenance strategy and protection of human lives and properties. This paper presents an automatic deep learning method using an improved fully Convolutional Neural Network (FCN) model based on <italic>U</italic>-Net architecture to detect and segment cracks on rail track images. An approach to evaluating the extent of damage on rail tracks is also proposed to aid efficient rail track maintenance. The model performance is evaluated using precision, recall, F1-Score, and Mean Intersection over Union (MIoU). The results obtained from the extensive analysis show <italic>U</italic>-Net capability to extract meaningful features for accurate crack detection and segmentation.</p>
</abstract>

Rail track breakages represent broken structures consisting of rail track on the railroad. The traditional methods for detecting this problem have proven unproductive. The safe operation of rail transportation needs to be frequently monitored because of the level of trust people have in it and to ensure adequate maintenance strategy and protection of human lives and properties. This paper presents an automatic deep learning method using an improved fully Convolutional Neural Network (FCN) model based on U-Net architecture to detect and segment cracks on rail track images. An approach to evaluating the extent of damage on rail tracks is also proposed to aid efficient rail track maintenance. The model performance is evaluated using precision, recall, F1-Score, and Mean Intersection over Union (MIoU). The results obtained from the extensive analysis show U-Net capability to extract meaningful features for accurate crack detection and segmentation.

A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network"}

Rail track breakages represent broken structures consisting of rail track on the railroad. The traditional methods for detecting this problem have proven...

A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 80 - 86

DOI: https://doi.org/10.2478/acss-2021-0010

Schlüsselwörter
Deep learning, fully convolutional neural network, rail track breakage, -Net architecture

© 2021 Olufunke Rebecca Vincent et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 80 - 86

DOI: https://doi.org/10.2478/acss-2021-0010

SchlüsselwörterDeep learning, fully convolutional neural network, rail track breakage, -Net architecture

© 2021 Olufunke Rebecca Vincent et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Deep learning, fully convolutional neural network, rail track breakage, -Net architecture