Year: 2025
Type: conference-paper
Abstract: Stripe-like space target detection (SSTD) plays a key role in enhancing space situational awareness, but it faces three challenges: the lack of public datasets, interference from space noise, and the variability of stripe-like targets, making manual labeling both inaccurate and labor-intensive. In response, we introduce ‘AstroStripeSet’, a pioneering dataset for SSTD, aiming to bridge the gap in academic resources. 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. It starts with generating initial pseudo-labels using the zero-shot capabilities of the Segment Anything Model (SAM) in a single-point setting. Then, the fine-tuned StripeSAM serves as the teacher and the newly developed StripeNet as the student, improving segmentation performance through label evolution, which iteratively refines these labels. We also introduce ‘GeoDice’, a new loss function tailored for the linear characteristics of stripe-like targets. Extensive experiments show that our method matches fully supervised approaches, exhibits strong zero-shot generalization for diverse real-world images, and sets a new state-of-the-art benchmark. Our dataset and code are available at https://github.com/BenZae/SSTD.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: China Scholarship Council
OpenAlex ID: W4415708484