To realise the rational utilisation of inland waterway resources, the intelligent identification method based on Convolutional Neural Network (CNN) is used to track and monitor the ships. Introducing the Repulsion Loss function and Soft-NMS algorithm to improve model, improve the detection precision of the partially occluded ships. The Feature Pyramid Networks (FPN) is used to realise the fusion of semantic information and spatial information of feature map to solve the problem of difficult detection of small object ships. Three up-sampling methods are used to extend and smooth the feature map. Through the above multiple algorithm improvements, the partial occlusion ships and small object ships in inland waterways are effectively detected.


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    Titel :

    Object detection of inland waterway ships based on improved SSD model


    Beteiligte:
    Yang, Yang (Autor:in) / Chen, Pengyu (Autor:in) / Ding, Kaifa (Autor:in) / Chen, Zhuang (Autor:in) / Hu, Kaixuan (Autor:in)

    Erschienen in:

    Ships and Offshore Structures ; 18 , 8 ; 1192-1200


    Erscheinungsdatum :

    2023-08-03


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





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