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HGSQ: Heatmap-Guided Sparse Query Detector for Real-Time Aerial Small Object Detection — arXiv:2609.13306

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13306
License (as archived): CC0-1.0
Archived: 2026-10-16T01:52:38.800Z
SHA-256 of the archived text: 549c92a3e28cb55eac30034b2dde7429344d52d80fb80ab3facdf8764fc3e599
Stable URL of this page: https://metraipolis.com/documento/dc_fefecabba2b6

How to cite

HGSQ: Heatmap-Guided Sparse Query Detector for Real-Time Aerial Small Object Detection — arXiv:2609.13306 — arXiv (en), https://arxiv.org/abs/2609.13306 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fefecabba2b6 (Metraipolis archive dc_fefecabba2b6, sha256 549c92a3e28cb55eac30034b2dde7429344d52d80fb80ab3facdf8764fc3e599)

BibTeX

@misc{dcfefecabba2b6,
  title = {HGSQ: Heatmap-Guided Sparse Query Detector for Real-Time Aerial Small Object Detection — arXiv:2609.13306},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13306},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fefecabba2b6; sha256 549c92a3e28cb55eac30034b2dde7429344d52d80fb80ab3facdf8764fc3e599},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13306. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Real-time aerial small object detection is an important visual signal and image processing problem, requiring a detector to preserve fine-grained localization while avoiding redundant computation on large background regions. This paper focuses on this deployment-oriented aerial/UAV setting rather than claiming a universal detector for all object detection scenarios. Existing Transformer-based detectors provide strong global modeling, but their dense query initialization and multi-layer decoder still spend substantial computation on background tokens, which is inefficient when small objects occupy only sparse image regions. To address this problem, this paper proposes HGSQ, a Heatmap-Guided Sparse Query Detector for real-time aerial small object detection. HGSQ uses a lightweight Heatmap Budget Predictor (HBP) to predict a foreground budget map in a single forward pass. The predicted heatmap is then used by three fixed components: Heatmap-Guided Sparse Query Selection (HSQS), which initializes decoder queries from high-confidence foreground positions; Heatmap-Gated Lite Snake Convolution (HGLSConv), which performs local shape refinement only on heatmap-activated small-object regions; and Adaptive Query-Decoder Budgeting (AQDB), which adjusts the query budget and decoder depth according to the estimated object density. Unlike post-hoc heatmap generation, HGSQ treats the heatmap as a real-time computation budget rather than a visualization map during deployment. Experiments on NWPU VHR-10 and VisDrone2019 show that HGSQ achieves 95.10 mAP50 on NWPU VHR-10 and 54.8 mAP50 on VisDrone2019, while reducing GFLOPs to 48.6 and running at 96.0 FPS on an RTX 4070 under our TensorRT FP16 deployment protocol.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-15 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.13306 — 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 2026-10-16T01:25:23.502Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).