WDICD: A novel simulated dataset and structure-aware framework for semantic segmentation of spacecraft component

Abstract

The identification and localization of non-cooperative target, especially for the components such as solar panel and docking ring, is crucial for missions involving the on-orbit space services, navigation, and positioning. However, on-orbit spacecraft images are difficult to obtain, and it requires substantial effort to label them with pixel-wise accuracy for semantic segmentation. To address the above problems, we construct a simulated Wide-Depth Illumination-Composite Dataset (WDICD) and introduces an automatic pixel-level semantic annotation method. Furthermore, this paper proposes a structure-aware framework for semantic segmentation of spacecraft component, which introduces a boundary-aware auxiliary task and constructs a boundary-distance-aware contrast loss based on batch-wise grouping. The related boundary-aware tasks alleviate the prediction error of semantic segmentation near the boundary. Experimental results demonstrate that the proposed method achieves SOTA mIoU results in spacecraft component segmentation against complex backgrounds on two datasets. Particularly, the test results on spaceborne device show that the proposed framework balances precision and efficiency without generating additional computational complexity.