轻量级无人机小目标检测模型，提升复杂环境下的识别精度
FRFDet: Efficient UAV Small Object Detection with Symmetric Sampling and Scalable Fusion
- 采用双向采样与可扩展融合模块，增强细节保留和特征对齐
- 在多个数据集上超越现有轻量模型，参数少、推理快、准确率高
- 适合计算资源受限的无人机平台部署，尤其适用于恶劣天气场景
无人机图像中的小目标检测在复杂天气、低照度和传感器噪声下仍具挑战，主要源于严重背景杂波、细粒度细节退化以及语义-空间特征融合不佳。为此，我们提出FRFDet，一种专为无人机小目标检测设计的轻量级单阶段检测器。引入两个即插即用模块：逆向双向采样（IBS）通过通道扩展-压缩与双向模式重建，保留关键空间细节，提升特征对齐；尺度-特征关系交叉融合（SFRCF）显式建模尺度依赖的融合行为，发现组间逐元素乘法适合紧凑模型，而组间加性融合更利于大型架构。在VisDrone、UAVDT、HazyDet和MS COCO上的大量实验表明，FRFDet在轻量级检测器中达到最新水平，计算开销低、参数少、推理快，非常适合资源受限的无人机平台。
原文摘要 · Abstract (English)
Small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging under adverse conditions, including complex weather, low illumination, and sensor noise. These challenges mainly stem from severe background clutter, fine-grained detail degradation, and suboptimal semantic-spatial feature fusion, which jointly hinder robust small-object representation. To this end, we propose FRFDet, a lightweight yet effective single-stage detector tailored for UAV-based small object detection. FRFDet proposes two plug-and-play modules: Inverse Bidirectional Sampling (IBS) and Scale-Feature Relationship Cross-Fusion (SFRCF). IBS preserves critical spatial details via channel expansion-compression and bidirectional pattern reconstruction, improving feature alignment. SFRCF explicitly models scale-dependent fusion behaviors, revealing that inter-group element-wise multiplication favors compact models, while inter-group additive fusion benefits larger architectures. Extensive experiments on VisDrone, UAVDT, HazyDet, and MS COCO demonstrate that FRFDet achieves state-of-the-art performance among lightweight detectors with low computational cost, compact parameters, and fast inference, making it well suited for resource-constrained UAV platforms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。