针对无人机图像小目标检测，提出可变感受野的高效检测框架。
An Efficient Aerial Image Detection with Variable Receptive Fields
- 引入动态空间注意力与门控多尺度融合，自适应调整感受野。
- 仅用13.5M参数实现51.4% mAP50，超越现有方法效率与精度平衡。
- 适合资源受限的实时无人机目标检测场景，尤其小目标密集区域。
基于无人机的航拍目标检测面临子10像素目标、密集遮挡及严格计算约束等挑战。现有检测器因感受野固定和结构冗余，难以兼顾精度与效率。为此，本文提出基于Transformer的可变感受野检测器（VRF-DETR），包含三个核心组件：1）多尺度上下文融合（MSCF）模块，通过自适应空间注意力与门控多尺度融合动态重校准特征；2）门控卷积（GConv）层，利用深度可分离操作与动态门控实现参数高效的局部上下文建模；3）门控多尺度融合（GMCF）瓶颈，通过级联的全局-局部交互分层解耦遮挡物体。在VisDrone2019数据集上的实验表明，VRF-DETR以仅13.5M参数达到51.4% mAP50与31.8% mAP50:95，建立了无人机检测任务的新效率-精度帕累托前沿。
原文摘要 · Abstract (English)
Aerial object detection using unmanned aerial vehicles (UAVs) faces critical challenges including sub-10px targets, dense occlusions, and stringent computational constraints. Existing detectors struggle to balance accuracy and efficiency due to rigid receptive fields and redundant architectures. To address these limitations, we propose Variable Receptive Field DETR (VRF-DETR), a transformer-based detector incorporating three key components: 1) Multi-Scale Context Fusion (MSCF) module that dynamically recalibrates features through adaptive spatial attention and gated multi-scale fusion, 2) Gated Convolution (GConv) layer enabling parameter-efficient local-context modeling via depthwise separable operations and dynamic gating, and 3) Gated Multi-scale Fusion (GMCF) Bottleneck that hierarchically disentangles occluded objects through cascaded global-local interactions. Experiments on VisDrone2019 demonstrate VRF-DETR achieves 51.4\% mAP\textsubscript{50} and 31.8\% mAP\textsubscript{50:95} with only 13.5M parameters. This work establishes a new efficiency-accuracy Pareto frontier for UAV-based detection tasks.
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