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CLSC DETR: Reliable Candidate Ranking via Cross Layer Geometric Support for UAV Small Object Detection — arXiv:2608.21457

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.21457
License (as archived): CC0-1.0
Archived: 2026-10-18T08:56:34.227Z
SHA-256 of the archived text: 8471fcbd6d62ad1b2c245d3b09439a94a9444221aa34a875483eaca13c05874b
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How to cite

CLSC DETR: Reliable Candidate Ranking via Cross Layer Geometric Support for UAV Small Object Detection — arXiv:2608.21457 — arXiv (en), https://arxiv.org/abs/2608.21457 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3bd2d10097dc (Metraipolis archive dc_3bd2d10097dc, sha256 8471fcbd6d62ad1b2c245d3b09439a94a9444221aa34a875483eaca13c05874b)

BibTeX

@misc{dc3bd2d10097dc,
  title = {CLSC DETR: Reliable Candidate Ranking via Cross Layer Geometric Support for UAV Small Object Detection — arXiv:2608.21457},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.21457},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3bd2d10097dc; sha256 8471fcbd6d62ad1b2c245d3b09439a94a9444221aa34a875483eaca13c05874b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.21457. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Unmanned aerial vehicle (UAV) object detection is critical for applications such as target search, where accurate detection of small objects in complex aerial scenes remains challenging. The limited spatial extent, dense distribution, and frequent occlusion of small objects make reliable candidate ranking particularly difficult. Existing Detection Transformer (DETR) based methods improve ranking by estimating localization quality from individual queries and incorporating it into classification scores. However, a single query often lacks sufficient geometric evidence for small objects with weak boundary cues, resulting in unreliable quality estimation and unstable ranking. To address this limitation, we propose Cross Layer Local Support and Consistency Calibration for DETR, termed CLSC DETR. Specifically, the Cross Layer Local Support module establishes correspondences between final layer queries and intermediate layer candidates to aggregate complementary geometric evidence for more reliable localization quality estimation, while the Classification and Localization Consistency Calibration module adaptively adjusts classification scores according to localization quality and classification reliability to improve candidate ranking. Experiments show that CLSC DETR improves AP and AP$_{75}$ over the baseline by 1.5\% and 2.0\% on VisDrone, respectively, while achieving consistent improvements on UAVDT.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-25 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.21457 — 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 2026-10-18T08:48:09.089Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).