A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based Methods
Efficiently detecting intrusions on a railway perimeter is crucial for ensuring the safety of railway transportation. With the development of computer vision, researchers have been actively exploring methods for detecting foreign object intrusion via image recognition technology. This article review...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10777007/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850060599629709312 |
|---|---|
| author | Jin Wang Hongyang Zhai Yang Yang Niuqi Xu Hao Li Di Fu |
| author_facet | Jin Wang Hongyang Zhai Yang Yang Niuqi Xu Hao Li Di Fu |
| author_sort | Jin Wang |
| collection | DOAJ |
| description | Efficiently detecting intrusions on a railway perimeter is crucial for ensuring the safety of railway transportation. With the development of computer vision, researchers have been actively exploring methods for detecting foreign object intrusion via image recognition technology. This article reviews the background and importance of detecting railway perimeter intrusion, summarizes the limitations of traditional detection methods, and emphasizes the potential of improving detection accuracy and efficiency in image recognition with deep learning models. Further, it introduces the development of deep learning in image recognition, focusing on the principles and progress of key technologies such as convolutional neural networks (CNNs) and vision transformers (ViTs). In addition, the application status of semantic segmentation and object detection algorithms based on deep learning in detecting railway perimeter intrusion is explored, including the classification, principles, and performance of the algorithms in practical applications. Finally, it highlights the primary challenges faced in railway perimeter intrusion detection and projects future research directions to resolve these challenges, including multisource data fusion, large-scale dataset construction, model compression, and end-to-end multitask learning networks. These studies support the accuracy and real-time detection of railway perimeter intrusion, and provide technical guarantees for railway transportation monitoring tasks. |
| format | Article |
| id | doaj-art-1baefd7f8dba40c4ab3e2c12e3e559f5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1baefd7f8dba40c4ab3e2c12e3e559f52025-08-20T02:50:30ZengIEEEIEEE Access2169-35362024-01-011218414218415710.1109/ACCESS.2024.351074610777007A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based MethodsJin Wang0https://orcid.org/0000-0002-1531-0912Hongyang Zhai1Yang Yang2https://orcid.org/0000-0002-2731-8014Niuqi Xu3Hao Li4Di Fu5Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, ChinaBeijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing, ChinaBeijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, ChinaBeijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing, ChinaBeijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing, ChinaBeijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing, ChinaEfficiently detecting intrusions on a railway perimeter is crucial for ensuring the safety of railway transportation. With the development of computer vision, researchers have been actively exploring methods for detecting foreign object intrusion via image recognition technology. This article reviews the background and importance of detecting railway perimeter intrusion, summarizes the limitations of traditional detection methods, and emphasizes the potential of improving detection accuracy and efficiency in image recognition with deep learning models. Further, it introduces the development of deep learning in image recognition, focusing on the principles and progress of key technologies such as convolutional neural networks (CNNs) and vision transformers (ViTs). In addition, the application status of semantic segmentation and object detection algorithms based on deep learning in detecting railway perimeter intrusion is explored, including the classification, principles, and performance of the algorithms in practical applications. Finally, it highlights the primary challenges faced in railway perimeter intrusion detection and projects future research directions to resolve these challenges, including multisource data fusion, large-scale dataset construction, model compression, and end-to-end multitask learning networks. These studies support the accuracy and real-time detection of railway perimeter intrusion, and provide technical guarantees for railway transportation monitoring tasks.https://ieeexplore.ieee.org/document/10777007/Railwaysemantic segmentationobject detectionforeign object intrusionrailway safety |
| spellingShingle | Jin Wang Hongyang Zhai Yang Yang Niuqi Xu Hao Li Di Fu A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based Methods IEEE Access Railway semantic segmentation object detection foreign object intrusion railway safety |
| title | A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based Methods |
| title_full | A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based Methods |
| title_fullStr | A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based Methods |
| title_full_unstemmed | A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based Methods |
| title_short | A Review of Intrusion Detection for Railway Perimeter Using Deep Learning-Based Methods |
| title_sort | review of intrusion detection for railway perimeter using deep learning based methods |
| topic | Railway semantic segmentation object detection foreign object intrusion railway safety |
| url | https://ieeexplore.ieee.org/document/10777007/ |
| work_keys_str_mv | AT jinwang areviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT hongyangzhai areviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT yangyang areviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT niuqixu areviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT haoli areviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT difu areviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT jinwang reviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT hongyangzhai reviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT yangyang reviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT niuqixu reviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT haoli reviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods AT difu reviewofintrusiondetectionforrailwayperimeterusingdeeplearningbasedmethods |