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TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection — arXiv:2609.08659

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08659
License (as archived): CC0-1.0
Archived: 2026-10-16T15:12:19.322Z
SHA-256 of the archived text: 7075e551c5cc544d0f8e5d3b1b9348b41152b372141497a8bc3f8330a39d75c4
Stable URL of this page: https://metraipolis.com/documento/dc_c262de8913ef

How to cite

TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection — arXiv:2609.08659 — arXiv (en), https://arxiv.org/abs/2609.08659 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c262de8913ef (Metraipolis archive dc_c262de8913ef, sha256 7075e551c5cc544d0f8e5d3b1b9348b41152b372141497a8bc3f8330a39d75c4)

BibTeX

@misc{dcc262de8913ef,
  title = {TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection — arXiv:2609.08659},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08659},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c262de8913ef; sha256 7075e551c5cc544d0f8e5d3b1b9348b41152b372141497a8bc3f8330a39d75c4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08659. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Onboard object detection in Earth observation is constrained by limited computational resources and the absence of fully corrected imagery. While convolutional detectors are hardware-efficient, they often struggle to extract robust representations from raw and noisy data. Conversely, transformer-based models provide stronger global reasoning capabilities but remain difficult to deploy on FPGA accelerators due to quadratic attention complexity and non-compatible operations. We introduce TriCCOT, a tri-part architecture for robust and deployable onboard object detection. TriCCOT combines a convolutional region proposal network, a conformal prediction stage, and Aper-GATES, our hardware-friendly attention-based classifier. The region proposal network generates candidate bounding boxes, which are subsequently enlarged via conformal prediction, providing a distribution-free probabilistic coverage guarantee. The resulting crops are processed by Aper-GATES, which reformulates self-attention through convolutional projections, global channel statistics, and hardware-friendly gating operations, avoiding standard transformer operations that are poorly suited to CNN-oriented accelerators. Experiments on the DIOR and VDVRaw datasets demonstrate competitive detection performance and improved robustness to spatial blur and signal-dependent noise when compared to FPGA-compatible architectures. Finally, we report full deployment on a Xilinx Versal VCK190 FPGA without modifying the underlying DPU architecture, enabling unified CNN-Transformer inference for spaceborne embedded applications.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-09 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.08659 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T14:49:57.333Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).