To ensure the navigational safety of bridge areas, a proactive ship early warning method integrating visual data and the Automatic Identification System (AIS) is proposed. Based on the DeepSORT algorithm, a visual measurement model and method for the target distance and bearing in the bridge area waters is established, which can address the issues of target ship loss and frequent ID information changes. By integrating visual and AIS data, a ship navigation situation model is constructed to detect abnormal behavior, allowing for the identification and automatic monitoring of dangerous ships in the bridge area waters. Experimental results demonstrate the effectiveness of this method in monitoring the movement status of ships in the bridge area, providing a reliable approach to ensure the safety of ships and bridges.


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    Titel :

    Study on Ship-Bridge Collision Prevention of Early Warning Based on Multi-Source Data Fusion


    Beteiligte:
    Zhao, Xingya (Autor:in) / Mou, Junmin (Autor:in) / Du, Zijun (Autor:in) / Zhang, Ke (Autor:in) / Wang, Bing (Autor:in) / Liu, Xiao (Autor:in)


    Erscheinungsdatum :

    08.11.2024


    Format / Umfang :

    582686 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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