This article proposes a ship target recognition method based on FAST detector and Faster-RCNN. Firstly, the FAST detector is used to extract the feature points of the ship target. Then, using the method of increasing the sliding window, improving the convolutional layer structure of Faster R-CNN, using suitable anchor points to identify the target; designed a recognition method based on the combination of the real-world model identification frame and the area suggestion to obtain the target information. Finally, the method of non-maximum suppression is used to filter and remove the redundant rectangular identification frame, so as to realize the accurate identification of the ship's real-world target. Through experimental comparison and analysis, this method has application advantages in extracting feature points with greater recognition utility and recognition rate.


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    Titel :

    SHIP target image recognition based on FAST detector and faster-RCNN


    Beteiligte:
    Zhao, Leyuan (Autor:in) / Liu, Bihui (Autor:in) / Liu, Chuanhui (Autor:in)

    Kongress:

    2nd International Conference on Computer Vision, Image, and Deep Learning ; 2021 ; Liuzhou,China


    Erschienen in:

    Proc. SPIE ; 11911


    Erscheinungsdatum :

    2021-10-05





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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