Version 1
: Received: 16 May 2024 / Approved: 17 May 2024 / Online: 17 May 2024 (11:35:56 CEST)
How to cite:
Ma, L.; Cao, H.; Shi, G.-Y.; Zhu, K.; Li, W.; Qin, S. A Ship State Recognition Method Based on Graph Convolutional Neural Network. Preprints2024, 2024051141. https://doi.org/10.20944/preprints202405.1141.v1
Ma, L.; Cao, H.; Shi, G.-Y.; Zhu, K.; Li, W.; Qin, S. A Ship State Recognition Method Based on Graph Convolutional Neural Network. Preprints 2024, 2024051141. https://doi.org/10.20944/preprints202405.1141.v1
Ma, L.; Cao, H.; Shi, G.-Y.; Zhu, K.; Li, W.; Qin, S. A Ship State Recognition Method Based on Graph Convolutional Neural Network. Preprints2024, 2024051141. https://doi.org/10.20944/preprints202405.1141.v1
APA Style
Ma, L., Cao, H., Shi, G. Y., Zhu, K., Li, W., & Qin, S. (2024). A Ship State Recognition Method Based on Graph Convolutional Neural Network. Preprints. https://doi.org/10.20944/preprints202405.1141.v1
Chicago/Turabian Style
Ma, L., Weifeng Li and Shengyan Qin. 2024 "A Ship State Recognition Method Based on Graph Convolutional Neural Network" Preprints. https://doi.org/10.20944/preprints202405.1141.v1
Abstract
To address the challenge of recognizing ship states accurately amidst the complexities of marine environments, this study proposes a novel ship state recognition approach leveraging a graph convolutional neural network (GCNN). Initially, the method extracts canonical and efficient ship motion trajectories from AIS data. Subsequently, a state recognition network tailored for ship motion trajectories is devised and implemented employing graph convolution. Notably, the accuracy of this model is enhanced through the introduction of novel weights and optimization of the Adj parameter. Experimental evaluations conducted on a ship state dataset demonstrate significant performance improvements. Specifically, the proposed recognition network achieves a recognition accuracy of 98.3% for regulated ship trajectories, marking an impressive 8.4% enhancement over traditional convolutional neural networks. This advancement holds promise for enhancing ship state recognition accuracy across diverse maritime applications including maritime supervision, navigation safety, and ship management.
Keywords
Maritime transportation networks; graph convolutional neural networks; AIS data
Subject
Environmental and Earth Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
