SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervision
Zijian Zhu, Ali Zia, Xuesong Li, Bingbing Dan, Yuebo Ma, Enhai Liu,, Rujin Zhao

TL;DR
This paper introduces a new dataset and a teacher-student framework for detecting stripe-like space targets with minimal supervision, achieving state-of-the-art results and strong zero-shot generalization.
Contribution
The paper presents AstroStripeSet, a pioneering dataset, and a novel label evolution framework with single-point weak supervision for space target detection.
Findings
Achieves performance comparable to fully supervised methods
Exhibits strong zero-shot generalization on real-world images
Sets new state-of-the-art benchmark in SSTD
Abstract
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 makes manual labeling both inaccurate and labor-intensive. 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 teacher-student label evolution framework with single-point weak supervision, providing a new solution to the challenges of manual labeling. This framework starts with generating initial pseudo-labels using the zero-shot capabilities of the Segment Anything Model (SAM) in a single-point setting. After that, the fine-tuned StripeSAM serves…
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Taxonomy
TopicsInfrared Target Detection Methodologies · CCD and CMOS Imaging Sensors · Advanced Neural Network Applications
