  Stripe-like space target detection (SSTD) plays a key role in enhancing space
situational awareness and assessing spacecraft behaviour. This domain faces
three challenges: the lack of publicly available datasets, interference from
stray light and stars, and the variability of stripe-like targets, which
complicates pixel-level annotation. In response, we introduces
`AstroStripeSet', a pioneering dataset designed for SSTD, aiming to bridge the
gap in academic resources and advance research in SSTD. Furthermore, we propose
a novel pseudo-label evolution teacher-student framework with single-point
supervision. This framework starts with generating initial pseudo-labels using
the zero-shot capabilities of the Segment Anything Model (SAM) in a
single-point setting, and refines these labels iteratively. In our framework,
the fine-tuned StripeSAM serves as the teacher and the newly developed
StripeNet as the student, consistently improving segmentation performance by
improving the quality of pseudo-labels. We also introduce `GeoDice', a new loss
function customized for the linear characteristics of stripe-like targets.
Extensive experiments show that the performance of our approach matches fully
supervised methods on all evaluation metrics, establishing a new
state-of-the-art (SOTA) benchmark. Our dataset and code will be made publicly
available.
