This paper proposes a warship image segmentation algorithm based on Mask RCNN network. Based on the Tensorflow+ Keral deep learning framework, the Mask-RCNN network structure was constructed. The segmentation of the image of warship at sea level was achieved by using the supervised learning method and tagging of the data set. Mask R-CNN is the most advanced convolutional neural network algorithm, which is mainly used for object detection and object instance segmentation of natural images. Due to the difficulty in obtaining warship samples and the insufficient number of data sets, the method of data enhancement is adopted to expand the data set. Through parameter adjustment and experimental verification, the mAP of warship reaches 0.603, which can meet the requirements of high-precision segmentation. The experimental results show that the Mask RCNN model has a very good effect on the image segmentation of naval ships at sea.


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    Titel :

    The segmentation algorithm of marine warship image based on mask RCNN


    Beteiligte:
    Chen, Hongyan (Autor:in) / Yu, Xinle (Autor:in)

    Kongress:

    Twelfth International Conference on Signal Processing Systems ; 2020 ; Shanghai,China


    Erschienen in:

    Proc. SPIE ; 11719


    Erscheinungsdatum :

    20.01.2021





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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