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.
SSP: A Large-Scale Semi-Real Dataset for Semantic Segmentation of Spacecraft Payloads
2023-07-27
6927218 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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