mlsanityData to 14 Oct 2026

MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection

arXiv 2506.12697 ↗First posted 15 Jun 2025cs.CV cs.AI cs.LGcited by 1

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Abstract (arXiv)

Small-object detection in Unmanned Aerial Vehicle (UAV) imagery requires preserving weak local evidence while using broader context to separate tiny foreground targets from cluttered backgrounds. Existing multi-scale fusion methods improve feature aggregation, but they often add computation or blur fine details during repeated cross-scale fusion. The central challenge is to balance low-SNR target preservation, clutter suppression, and efficient cross-scale context exchange. To address this challenge, we propose the Multi-scale Global-detail Feature Integration Strategy (MGDFIS), a neck-level feature-fusion strategy that couples global context exchange, local-detail recovery, and pixel-level foreground-background recalibration. MGDFIS integrates three coordinated modules: FusionLock-TSS Attention for stabilizing spectral-spatial responses, Global-detail Integration for combining long-range mixing with local detail capture, and Dynamic Pixel Attention for reweighting compact foreground regions. On the controlled VisDrone setting, YOLO26m + MGDFIS improves AP50:95 from 25.7 to 30.2 and AP50 from 37.2 to 44.2 over the YOLO26m baseline, with 96.1 GFLOPs. Additional dataset-specific evaluations report 38.9 AP50 and 21.9 AP50:95 on UAVDT and 97.4 AP50 on CARPK. The code is available at: https://github.com/Bai-Xuecheng/MGDFIS.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 36 of its 66 references that are in the map. First posted on arXiv 15 Jun 2025.

Builds on

36 of 66 references.

References · 36

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24 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026CollabOD: Collaborative Multi-Backbone with Cross-scale Vision for UAV Small Object Detectioncited by 0