Vessel Trajectory Anomaly Detection Based on Multi-scale Convolutional Autoencoder
Traditional methods for detecting anomalies in vessel trajectories do not adequately account for the multidimensional characteristics of vessel behavior, and lack the ability to capture trajectory details. To address these issues, a method of vessel trajectory anomaly detection based on Multi-scale...
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| Vydáno v: | Ocean engineering Ročník 343; s. 123564 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
15.01.2026
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| Témata: | |
| ISSN: | 0029-8018 |
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| Abstract | Traditional methods for detecting anomalies in vessel trajectories do not adequately account for the multidimensional characteristics of vessel behavior, and lack the ability to capture trajectory details. To address these issues, a method of vessel trajectory anomaly detection based on Multi-scale Convolutional Autoencoder (MCAE) is proposed, which fully leverages the multidimensional behavioral features of vessels to enhance the precision of anomaly detection. Firstly, a mechanism for generating multidimensional features information trajectory maps is presented. Secondly, an MCAE model capable of effectively capturing trajectory details is constructed. Building upon the autoencoder, local and global features of trajectory maps at various resolutions are extracted through multi-scale convolution and fused into intermediate decoder layers by skip connections, achieving an enhanced ability to capture trajectory details. Finally, an algorithm of vessel trajectory anomaly detection based on MCAE is proposed. The Structural Similarity Index Measure (SSIM) is applied to calculate anomaly scores for maps reconstructed by the model, and a threshold is set to achieve the anomaly detection of vessel trajectories. The Port of Tianjin is used as the study area, and experimental results demonstrate that the proposed method effectively detects anomaly vessel trajectories, closely matching actual conditions. Compared to other methods, it shows superior reconstruction accuracy and detection precision, offering robust support for port security management and maritime traffic safety.
•A mechanism for generating multidimensional features trajectory maps is presented.•An MCAE model capable of effectively capturing trajectory details is constructed.•An algorithm of vessel trajectory anomaly detection based on MCAE is proposed.•The proposed method is verified through real case analysis and model comparison. |
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| AbstractList | Traditional methods for detecting anomalies in vessel trajectories do not adequately account for the multidimensional characteristics of vessel behavior, and lack the ability to capture trajectory details. To address these issues, a method of vessel trajectory anomaly detection based on Multi-scale Convolutional Autoencoder (MCAE) is proposed, which fully leverages the multidimensional behavioral features of vessels to enhance the precision of anomaly detection. Firstly, a mechanism for generating multidimensional features information trajectory maps is presented. Secondly, an MCAE model capable of effectively capturing trajectory details is constructed. Building upon the autoencoder, local and global features of trajectory maps at various resolutions are extracted through multi-scale convolution and fused into intermediate decoder layers by skip connections, achieving an enhanced ability to capture trajectory details. Finally, an algorithm of vessel trajectory anomaly detection based on MCAE is proposed. The Structural Similarity Index Measure (SSIM) is applied to calculate anomaly scores for maps reconstructed by the model, and a threshold is set to achieve the anomaly detection of vessel trajectories. The Port of Tianjin is used as the study area, and experimental results demonstrate that the proposed method effectively detects anomaly vessel trajectories, closely matching actual conditions. Compared to other methods, it shows superior reconstruction accuracy and detection precision, offering robust support for port security management and maritime traffic safety.
•A mechanism for generating multidimensional features trajectory maps is presented.•An MCAE model capable of effectively capturing trajectory details is constructed.•An algorithm of vessel trajectory anomaly detection based on MCAE is proposed.•The proposed method is verified through real case analysis and model comparison. |
| ArticleNumber | 123564 |
| Author | Xu, Dongsheng Qi, Yuhao Shao, Ran Wang, Yangjie Yang, Jiaxuan Duan, Yangyang |
| Author_xml | – sequence: 1 givenname: Yuhao orcidid: 0000-0002-1026-4427 surname: Qi fullname: Qi, Yuhao email: qiyuhao@dlmu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, China – sequence: 2 givenname: Jiaxuan orcidid: 0000-0002-0672-1330 surname: Yang fullname: Yang, Jiaxuan email: yangjiaxuan@dlmu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, China – sequence: 3 givenname: Dongsheng orcidid: 0009-0002-2901-0352 surname: Xu fullname: Xu, Dongsheng email: xds2121@dlmu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, China – sequence: 4 givenname: Ran orcidid: 0009-0002-5438-0857 surname: Shao fullname: Shao, Ran email: 851000166@qq.com organization: Navigation College, Dalian Maritime University, Dalian, 116026, China – sequence: 5 givenname: Yangyang surname: Duan fullname: Duan, Yangyang email: duanyy@dlmu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, China – sequence: 6 givenname: Yangjie surname: Wang fullname: Wang, Yangjie email: wangyangjie@dlmu.edu.cn organization: Navigation College, Dalian Maritime University, Dalian, 116026, China |
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| Keywords | Multi-scale convolutional autoencoder Vessel trajectory Multidimensional features Anomaly detection |
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