Abstract: Precise segmentation of satellite components is key to RPO (rendezvous and proximity operations) and OOS (on orbit servicing), while harsh space environment and compact layout of components hinder fine grained pixel wise recognition. An edge auxiliary supervised components segmentation network (EASCSN) is proposed to tackle these problems. First, a two branch encoder is designed where pyramidal spatial features and global semantic features are fused with gated semantic injection module in a cascade manner. Second, a slim but strong decoder with convolution free feature aggregation module is elaborately designed so that high quality parts of multi-scale features are distilled and aggregated. Meanwhile, an auxiliary edge supervised strategy is adopted during training for sharper prediction in the edge regions. Massive experiments demonstrate the superiority of the proposed EASCSN. With compact model size and low computation cost, EASCSN can achieve a new state of the art speed accuracy tradeoff. Specifically, on a single Tesla T4 GPU, EASCSN yields 74.74% mIoU and 80.99% mAcc at 43.29 FPS on UESD test set. Efficient satellite components recognization helps perceive structure of target satellites and achieve space intelligent control. There is potential value of being further deployed to spaceborne platforms.

Key words: satellite components, semantic segmentation, light weight design, edge supervision