The development and utilization of marine resources by mankind has brought out a series of practical problems such as the destruction of marine ecology, the damage of seabed assets, and the disputes over marine sovereignty. How to use information technology tools to profile and monitor ships, accurately classify and identify ship behaviors through multi-source data fusion analysis, and timely alert and invert abnormal behaviors have become an important means of intelligent ocean governance. In response to the above needs, this paper classifies the ship’s behavior, designs a new data structure ShipInfoSet that represents the ship’s multi-source heterogeneous spatio-temporal information, and proposes a deep learning-based ship behavior-monitoring algorithm ML-Dabs. Accurate identification of the ship’s behavior based on deep learning has realized the monitoring and warning of different types of ships’ profiles and abnormal behaviors. This paper designs an intelligent ocean information port architecture, which can be implemented by deploying the algorithm.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Intelligent Ocean Governance—Deep Learning-Based Ship Behavior Detection and Application


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liang, Qilian (editor) / Wang, Wei (editor) / Mu, Jiasong (editor) / Liu, Xin (editor) / Na, Zhenyu (editor) / Cai, Xiantao (editor) / Qin, Peng (author) / Cao, Yang (author)


    Publication date :

    2021-02-09


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Ship Detection Based on Deep Learning

    Wang, Yuchao / Ning, Xiangyun / Leng, Binghan et al. | British Library Conference Proceedings | 2019


    Ship Recognition and Tracking System for Intelligent Ship Based on Deep Learning Framework

    Bo Liu / Sheng Zheng Wang / Z.X. Xie et al. | DOAJ | 2019

    Free access

    Design of deep ocean drilling ship

    Bascom, W. / McLelland, J. / Lampietti, F. et al. | Engineering Index Backfile | 1962


    Tracking control of intelligent ship based on deep reinforcement learning

    Kang ZHU / Zhen HUANG / Xuming WANG | DOAJ | 2021

    Free access

    Arbitrary-Oriented Ship Detection based on Deep Learning

    Chen, Xingyu / Tang, Chaoying | IEEE | 2022