Semantic segmentation technology is widely used in autonomous driving, environment perception, and other fields. With the development of deep learning technology, semantic segmentation will also be applied to spacecraft-related research (i.e., spacecraft-payload segmentation). However, the lack of spacecraft-related datasets seriously hinders the development of deep learning technology in research fields, such as spacecraft control and payload segmentation. To address this issue, we release a novel semi-real dataset for the segmentation of spacecraft payload. Specifically, the images in our released dataset are generated by merging images of spacecraft models taken by a real camera and space images released by the National Aeronautics and Space Administration (NASA). Furthermore, we propose a network SPSNet with an anti-pyramid-structured decoder for the segmentation of spacecraft payload. Experimental results show that our proposed network achieves superior performance compared with the well-known networks on our released dataset.


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    Titel :

    SSP: A Large-Scale Semi-Real Dataset for Semantic Segmentation of Spacecraft Payloads


    Beteiligte:
    Guo, Yanning (Autor:in) / Feng, Zhen (Autor:in) / Song, Bin (Autor:in) / Li, Xinglong (Autor:in)


    Erscheinungsdatum :

    2023-07-27


    Format / Umfang :

    6927218 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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