Research on Ship Track and Navigation Behavior Characteristics Based on Deep Learning

Yue Yang, Tianyi Liu, Xiaolong Wu

2023

Abstract

Along with the expansion of China’s Marine interests and the improvement of comprehensive national strength, Marine navigation safety and ship abnormal navigation behaviour problems are increasingly acute. In this paper, a deep learning CALS algorithm based on neural network is proposed to analyse more than AIS ship sailing track data in the Chinese sea area in 2021, and build ship sailing track prediction and early warning models. By exploring the navigation characteristics of various types of ships in the Chinese sea area and the characteristics of navigation behaviour at different time and space scales, this paper is to solve the problem of abnormal trajectory detection and early warning of ships, and provide information support for safe navigation and military mission decision-making.

Paper Citation

in Harvard Style

Yang Y., Liu T. and Wu X. (2023). Research on Ship Track and Navigation Behavior Characteristics Based on Deep Learning. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 370-374. DOI: 10.5220/0012284200003807

in Bibtex Style

@conference{anit23,
author={Yue Yang and Tianyi Liu and Xiaolong Wu},
title={Research on Ship Track and Navigation Behavior Characteristics Based on Deep Learning},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={370-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012284200003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Research on Ship Track and Navigation Behavior Characteristics Based on Deep Learning
SN - 978-989-758-677-4
AU - Yang Y.
AU - Liu T.
AU - Wu X.
PY - 2023
SP - 370
EP - 374
DO - 10.5220/0012284200003807
PB - SciTePress