Inland waterway shipping has the characteristics of a wide variety of ships, high vessel density, undulating waves, water mist influence, and complex shore and bridge environments. To ensure the safe and efficient operation of inland waterway shipping, it is necessary to establish an advanced intelligent shipping management system. The rapid and accurate detection and effective tracking of ship targets are the foundation and key technologies of an intelligent shipping management system. Its research is of great significance for improving the intelligent driving of ships and the management level of management departments. Traditional ship target detection algorithms have poor robustness, weak detection accuracy, and long-time consumption on complex inland waterways, which cannot meet the intelligent navigation management needs of ships. In recent years, with the flourishing of deep learning in various competitions, more and more researchers have used deep learning algorithms for ship detection and recognition. This work addresses the problems of traditional ship target detection algorithms and designs the YOLOv5 algorithm based on fusion frame difference method as the ship target detector. The improved DeepSORT tracking algorithm is used as the ship tracking module, and the two are combined to achieve automatic detection and tracking of inland ships.


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    Titel :

    Research on Improved YOLO and DeepSORT Ship Detection and Tracking Algorithms


    Beteiligte:
    Qi, Bohan (Autor:in) / Zhang, Pei (Autor:in) / Huang, Wenbin (Autor:in)


    Erscheinungsdatum :

    24.05.2024


    Format / Umfang :

    1900892 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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