Ship identification is of great significance in coastal area monitoring and port military reconnaissance. The characteristics of different types of ships in high-resolution images are highly similar, and the internal details of the target are clearer. In order to effectively use the target details in the high-resolution image for target recognition, based on the UAV images of Xingcheng wharf and bingjiawan in Huludao City, Liaoning Province, the UAV ship image data sets with coarse-grained and fine-grained classification modes are established respectively. Build a deep learning platform based on tensorflow, and use SSD algorithm to identify the ships in the data set. The recognition accuracy of ship image data set based on coarse-grained classification pattern is 90.87%; The accuracy of ship image data set based on fine-grained classification model is 94.63%. The results show that: SSD algorithm can realize ship recognition of UAV image and Image data set based on fine-grained classification pattern can effectively use the detailed features of high-resolution image targets, so as to improve the accuracy of ship recognition.


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    Title :

    Research on UAV Image Ship Recognition Based on Fine-grained Classification Data Set


    Contributors:
    Su, Chunqing (author) / Pan, Jun (author) / Jiang, Lijun (author) / Sun, Yehan (author) / Yu, Wei (author) / Cao, Yu (author)


    Publication date :

    2021-11-12


    Size :

    4326126 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English