Tracing segmentation for satellite partial components under low-light environment

Zhuang Xu; Yang Zhang; Jingmin Gao; Kebei Zhang; Miao Guo

doi:10.1117/12.2604717

5 October 2021 Tracing segmentation for satellite partial components under low-light environment

Zhuang Xu, Yang Zhang, Jingmin Gao, Kebei Zhang, Miao Guo

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119111K (2021) https://doi.org/10.1117/12.2604717
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

The lack of lighting in the space environment results in low segmentation accuracy and target lost. To solve this problem, a satellite component tracking method based on Few-Shot learning is proposed in this paper. First, we design a convolutional neural network, which inputs the first frame of mask information, and outputs the true label and important weight parameters. The Few-Shot learning incorporates the real labels, important weight parameters and the first frame feature information to generate target model parameters. Subsequent frames combine target model parameters with feature extraction, and finally output target mask after encoding and decoding. Our algorithm is evaluated on a new satellite partial component data set, and the simulation results show that the proposed method improves the segmentation accuracy and reduces the target loss rate compared to SiamMask under low-light environment.

Citation Download Citation

Zhuang Xu, Yang Zhang, Jingmin Gao, Kebei Zhang, and Miao Guo "Tracing segmentation for satellite partial components under low-light environment", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119111K (5 October 2021); https://doi.org/10.1117/12.2604717

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