Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene

Huang, H; Zhao, G; Bo, Y; Yu, J; Liang, LD; Yang, Y; Ou, K

Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene

Huang, H Zhao, G Bo, Y Yu, J Liang, LD Yang, Y

Ou, K

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Measurement: Journal of the International Measurement Confederation, 2023, 211
Issue Date:: 2023-04-01

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Field	Value	Language
dc.contributor.author	Huang, H
dc.contributor.author	Zhao, G
dc.contributor.author	Bo, Y
dc.contributor.author	Yu, J
dc.contributor.author	Liang, LD
dc.contributor.author	Yang, Y https://orcid.org/0000-0002-0512-880X
dc.contributor.author	Ou, K
dc.date.accessioned	2024-05-13T05:50:20Z
dc.date.available	2024-05-13T05:50:20Z
dc.date.issued	2023-04-01
dc.identifier.citation	Measurement: Journal of the International Measurement Confederation, 2023, 211
dc.identifier.issn	0263-2241
dc.identifier.issn	1873-412X
dc.identifier.uri	http://hdl.handle.net/10453/178937
dc.description.abstract	Railway intrusion detection is important for the safety of the railway operation. However, how to detect intruding events in the scene of unmanned aerial vehicle (UAV) surveillance is a challenging task, especially when there are multiple railway regions or unknown types of intruding objects. To tackle the above problems, a railway intrusion detection method is proposed for the scene of UAV surveillance. It can detect intruding events accurately without pre-setting the intruding object type by detecting the normal railway region and the abnormal railway region with intruding objects rather than recognizing the intruding objects directly. The proposed network consists of a novel Fused-convolutional long short-term memory (Fused-ConvLSTM) module for efficiently propagating spatial and temporal information; several attention modules for adaptively enhancing the feature information, such as the improved feature fusion layer; several lightweight strategies for reducing the computation complexity, such as lightweight feature extraction and width multiplier. Experiments are performed on the real aerial videos obtained by a micro UAV. The experimental results demonstrate that the proposed method can accurately detect railway intrusion events in aerial video, and effectively deal with the case of multiple railway regions and unknown intruding object types. The experimental results also demonstrate that the proposed network can achieve better performance than other object detection networks.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Measurement: Journal of the International Measurement Confederation
dc.relation.isbasedon	10.1016/j.measurement.2023.112602
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0913 Mechanical Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene
dc.type	Journal Article
utslib.citation.volume	211
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-13T05:50:14Z
pubs.publication-status	Published
pubs.volume	211

Abstract:

Railway intrusion detection is important for the safety of the railway operation. However, how to detect intruding events in the scene of unmanned aerial vehicle (UAV) surveillance is a challenging task, especially when there are multiple railway regions or unknown types of intruding objects. To tackle the above problems, a railway intrusion detection method is proposed for the scene of UAV surveillance. It can detect intruding events accurately without pre-setting the intruding object type by detecting the normal railway region and the abnormal railway region with intruding objects rather than recognizing the intruding objects directly. The proposed network consists of a novel Fused-convolutional long short-term memory (Fused-ConvLSTM) module for efficiently propagating spatial and temporal information; several attention modules for adaptively enhancing the feature information, such as the improved feature fusion layer; several lightweight strategies for reducing the computation complexity, such as lightweight feature extraction and width multiplier. Experiments are performed on the real aerial videos obtained by a micro UAV. The experimental results demonstrate that the proposed method can accurately detect railway intrusion events in aerial video, and effectively deal with the case of multiple railway regions and unknown intruding object types. The experimental results also demonstrate that the proposed network can achieve better performance than other object detection networks.

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http://hdl.handle.net/10453/178937