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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Bi, Qilin (Autor:in) / Wang, Miaohui (Autor:in) / Huang, Yijing (Autor:in) / Lai, Minling (Autor:in) / Liu, Zhijun (Autor:in) / Bi, Xiuying (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-11-01


    Format / Umfang :

    2784930 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Inland ship navigation collision avoidance method and system

    ZHANG XIAOHAN / WU HONGGUANG / ZHAI ZHENGANG et al. | Europäisches Patentamt | 2023

    Freier Zugriff


    Vision-Based Proportional Navigation for UAS Collision Avoidance

    Clark, Matthew / Prazenica, Richard J. | AIAA | 2016