A Review on Deep Learning Techniques for Railway Infrastructure Monitoring
In the last decade, thanks to a widespread diffusion of powerful computing machines, artificial intelligence has been attracting the attention of the academic and industrial worlds. This review aims to understand how the scientific community is approaching the use of deep-learning techniques in a pa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10233858/ |
| _version_ | 1797654026616045568 |
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| author | Maria Di Summa Maria Elena Griseta Nicola Mosca Cosimo Patruno Massimiliano Nitti Vito Reno Ettore Stella |
| author_facet | Maria Di Summa Maria Elena Griseta Nicola Mosca Cosimo Patruno Massimiliano Nitti Vito Reno Ettore Stella |
| author_sort | Maria Di Summa |
| collection | DOAJ |
| description | In the last decade, thanks to a widespread diffusion of powerful computing machines, artificial intelligence has been attracting the attention of the academic and industrial worlds. This review aims to understand how the scientific community is approaching the use of deep-learning techniques in a particular industrial sector, the railway. This work is an in-depth analysis related to the last years of the way this new technology can try to provide answers even in a field where the primary requirement is to improve the already very high levels of safety. A strategic and constantly evolving field such as the railway sector could not remain extraneous to the use of this new and promising technology. Deep learning algorithms devoted to the classification, segmentation, and detection of the faults that affect the railway area and the overhead contact system are discussed. The railway sector offers many aspects that can be investigated with these techniques. This work aims to expose the possible applications of deep learning in the railway sector established on the type of recovered information and the type of algorithms to be used accordingly. |
| first_indexed | 2024-03-11T16:53:24Z |
| format | Article |
| id | doaj.art-705fe90e6c9748be9f1be992319b52fb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T16:53:24Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-705fe90e6c9748be9f1be992319b52fb2023-10-20T23:00:23ZengIEEEIEEE Access2169-35362023-01-011111463811466110.1109/ACCESS.2023.330981410233858A Review on Deep Learning Techniques for Railway Infrastructure MonitoringMaria Di Summa0https://orcid.org/0000-0001-5167-867XMaria Elena Griseta1https://orcid.org/0000-0001-9733-3546Nicola Mosca2Cosimo Patruno3Massimiliano Nitti4Vito Reno5Ettore Stella6https://orcid.org/0000-0003-1770-1228Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, ItalyDepartment of Mathematics, University of Bari Aldo Moro, Bari, ItalyInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, ItalyInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, ItalyInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, ItalyInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, ItalyInstitute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, ItalyIn the last decade, thanks to a widespread diffusion of powerful computing machines, artificial intelligence has been attracting the attention of the academic and industrial worlds. This review aims to understand how the scientific community is approaching the use of deep-learning techniques in a particular industrial sector, the railway. This work is an in-depth analysis related to the last years of the way this new technology can try to provide answers even in a field where the primary requirement is to improve the already very high levels of safety. A strategic and constantly evolving field such as the railway sector could not remain extraneous to the use of this new and promising technology. Deep learning algorithms devoted to the classification, segmentation, and detection of the faults that affect the railway area and the overhead contact system are discussed. The railway sector offers many aspects that can be investigated with these techniques. This work aims to expose the possible applications of deep learning in the railway sector established on the type of recovered information and the type of algorithms to be used accordingly.https://ieeexplore.ieee.org/document/10233858/Anomaly detectiondeep learningrailwayreview |
| spellingShingle | Maria Di Summa Maria Elena Griseta Nicola Mosca Cosimo Patruno Massimiliano Nitti Vito Reno Ettore Stella A Review on Deep Learning Techniques for Railway Infrastructure Monitoring IEEE Access Anomaly detection deep learning railway review |
| title | A Review on Deep Learning Techniques for Railway Infrastructure Monitoring |
| title_full | A Review on Deep Learning Techniques for Railway Infrastructure Monitoring |
| title_fullStr | A Review on Deep Learning Techniques for Railway Infrastructure Monitoring |
| title_full_unstemmed | A Review on Deep Learning Techniques for Railway Infrastructure Monitoring |
| title_short | A Review on Deep Learning Techniques for Railway Infrastructure Monitoring |
| title_sort | review on deep learning techniques for railway infrastructure monitoring |
| topic | Anomaly detection deep learning railway review |
| url | https://ieeexplore.ieee.org/document/10233858/ |
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