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 unproductive. The safe operation of rail transportation needs to be frequently monitored because of the level of trust people have in it and to en...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2021-12-01
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| Series: | Applied Computer Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/acss-2021-0010 |
| _version_ | 1819290525247733760 |
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| author | Vincent Olufunke Rebecca Babalola Yetunde Ebunoluwa Sodiya Adesina Simon Adeniran Olusola John |
| author_facet | Vincent Olufunke Rebecca Babalola Yetunde Ebunoluwa Sodiya Adesina Simon Adeniran Olusola John |
| author_sort | Vincent Olufunke Rebecca |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T03:24:07Z |
| format | Article |
| id | doaj.art-c678bd808e0e4c3d9f43d110bad4797f |
| institution | Directory Open Access Journal |
| issn | 2255-8691 |
| language | English |
| last_indexed | 2024-12-24T03:24:07Z |
| publishDate | 2021-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Computer Systems |
| spelling | doaj.art-c678bd808e0e4c3d9f43d110bad4797f2022-12-21T17:17:25ZengSciendoApplied Computer Systems2255-86912021-12-01262808610.2478/acss-2021-0010A Cognitive Rail Track Breakage Detection System Using Artificial Neural NetworkVincent Olufunke Rebecca0Babalola Yetunde Ebunoluwa1Sodiya Adesina Simon2Adeniran Olusola John3Federal University of Agriculture, Abeokuta, NigeriaFederal University of Agriculture, Abeokuta, NigeriaFederal University of Agriculture, Abeokuta, NigeriaFederal University of Agriculture, Abeokuta, NigeriaRail 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.https://doi.org/10.2478/acss-2021-0010deep learningfully convolutional neural networkrail track breakageu-net architecture |
| spellingShingle | Vincent Olufunke Rebecca Babalola Yetunde Ebunoluwa Sodiya Adesina Simon Adeniran Olusola John A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network Applied Computer Systems deep learning fully convolutional neural network rail track breakage u-net architecture |
| title | A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network |
| title_full | A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network |
| title_fullStr | A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network |
| title_full_unstemmed | A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network |
| title_short | A Cognitive Rail Track Breakage Detection System Using Artificial Neural Network |
| title_sort | cognitive rail track breakage detection system using artificial neural network |
| topic | deep learning fully convolutional neural network rail track breakage u-net architecture |
| url | https://doi.org/10.2478/acss-2021-0010 |
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