Ship collision avoidance (SCA) is an important technique in the field of decision-making in marine navigation. Although some promising solutions have been developed recently, there is still the lack of low-cost and reliable sensing equipment. Inspired by the low-cost of camera sensors and the success of machine learning, this paper designs a vision-based method to recognize ships and their micro-features for SCA navigation planning. Firstly, we develop a vision-based bearing, distance and velocity model based on a wide-field optical imaging system. Secondly, optical information is used to construct the micro-characteristic imaging model of ship navigation signals. Thirdly, we have solved the problem between a large field-of-view (FOV) and high-resolution imaging in vision-based marine navigation. Finally, an improved Adaboost algorithm is designed for the intelligent recognition of an open-sea target (ship types and light patterns). The proposed method has been verified by extensive experiments in a practical environment, and the results show that it can effectively and efficiently identify the navigation signal of a target ship.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Ship Collision Avoidance Navigation Signal Recognition via Vision Sensing and Machine Forecasting


    Contributors:
    Bi, Qilin (author) / Wang, Miaohui (author) / Huang, Yijing (author) / Lai, Minling (author) / Liu, Zhijun (author) / Bi, Xiuying (author)

    Published in:

    Publication date :

    2023-11-01


    Size :

    2784930 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Inland ship navigation collision avoidance method and system

    ZHANG XIAOHAN / WU HONGGUANG / ZHAI ZHENGANG et al. | European Patent Office | 2023

    Free access


    Adaptive collision avoidance decision system for autonomous ship navigation

    Zhao, Xingya / He, Yixiong / Huang, Liwen et al. | Taylor & Francis Verlag | 2025



    Intelligent collision avoidance method and system for ship navigation

    YANG YUGUO / TAO LIANGSHEN / LI LEI et al. | European Patent Office | 2024

    Free access