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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2407.18097 |
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| _version_ | 1866909317166268416 |
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| author | Zhu, Zijian Zia, Ali Li, Xuesong Dan, Bingbing Ma, Yuebo Liu, Enhai Zhao, Rujin |
| author_facet | Zhu, Zijian Zia, Ali Li, Xuesong Dan, Bingbing Ma, Yuebo Liu, Enhai Zhao, Rujin |
| contents | 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 as the teacher and the newly developed StripeNet as the student, consistently improving segmentation performance through label evolution, which iteratively refines these labels. We also introduce `GeoDice', a new loss function customized 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 space-based and ground-based real-world images, and sets a new state-of-the-art (SOTA) benchmark. Our AstroStripeSet dataset and code will be made publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18097 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervision Zhu, Zijian Zia, Ali Li, Xuesong Dan, Bingbing Ma, Yuebo Liu, Enhai Zhao, Rujin Computer Vision and Pattern Recognition 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 as the teacher and the newly developed StripeNet as the student, consistently improving segmentation performance through label evolution, which iteratively refines these labels. We also introduce `GeoDice', a new loss function customized 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 space-based and ground-based real-world images, and sets a new state-of-the-art (SOTA) benchmark. Our AstroStripeSet dataset and code will be made publicly available. |
| title | SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervision |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.18097 |