Abstract: Small target detection in low-altitude UAV imagery is challenged by small object scales, dense spatial distributions, strong background interference, and complex imaging conditions. Compared with general objects, small targets occupy only a limited number of pixels, making their edges, textures, and shape cues prone to being overwhelmed by background responses during feature extraction. Existing detection methods usually obtain high-level semantic features through stacked convolutions and progressive downsampling. However, this process rapidly reduces the spatial resolution of feature maps, causing the local structures and pixel-level details of small targets to be weakened or even lost. Although feature pyramids and attention mechanisms can alleviate scale variation and background interference to some extent, most methods focus mainly on multi-scale semantic fusion and provide limited direct constraints on the fine-grained spatial information loss caused by downsampling. Meanwhile, insufficient collaborative modeling among channel features makes it difficult to integrate small-target cues scattered across different channels into stable and discriminative representations. To address these problems, this paper proposes RFINet, a small target detection network based on recalibrated fine-grained downsampling and inter-group collaborative modeling. First, a recalibrated fine-grained downsampling (RFD) module is constructed, which replaces conventional downsampling with spatial slicing and channel reorganization. By mapping local spatial information into the channel dimension, RFD preserves fine-grained structures of small targets while reducing the feature map size. To suppress redundant responses and background noise introduced by channel expansion, RFD further introduces a cross-channel adaptive recalibration mechanism that reallocates weights according to aggregated channel responses, thereby enhancing target-related channels and suppressing irrelevant background information. Compared with stride convolution or pooling, RFD does not simply discard spatial sampling points; instead, it transfers information that would otherwise be compressed into the channel domain through structured rearrangement, providing more complete local context for subsequent feature extraction. Second, an inter-group collaborative modeling (ICM) module is designed to divide input features into multiple channel subspaces, extract locally salient information within each group, and promote intra-group enhancement and inter-group interaction through weight redistribution, residual connection, and channel concatenation. This design preserves the fine-grained local modeling ability of grouped features while alleviating information isolation caused by fixed grouping, enabling the network to integrate small-target cues distributed across different channels more effectively. Finally, object classification and bounding box regression are performed by the detection head to achieve small target detection in complex low-altitude scenes. To verify the effectiveness of the proposed modules, experiments are conducted from multiple perspectives, including different input resolutions, ablation studies, model complexity, detection accuracy, and cross-dataset generalization, so as to evaluate the overall performance of RFINet under dense small-target distributions, significant scale variation, and strong background interference. Experimental results on the VisDrone2019 dataset show that RFD effectively preserves fine-grained spatial information of small targets and suppresses redundant background noise, while ICM reduces redundant background responses and strengthens the model’s focus on key feature regions. The combination of the two modules brings stable performance gains under different input resolutions. RFINet achieves mAP@0.5 scores of 40.8% and 47.5% with input sizes of 640×640 and 960×960, respectively, outperforming the compared mainstream two-stage detectors, one-stage detectors, and lightweight detection models. The results demonstrate that the proposed method enhances the representation of small targets in dense, occluded, and cluttered scenes while maintaining high detection accuracy, providing a more targeted feature modeling scheme for low-altitude UAV visual perception tasks.

摘要： 低空无人机航拍图像中的微小目标检测面临尺度小、分布密集、背景干扰强和成像条件复杂等问题。与一般目标相比，微小目标在图像中仅占少量像素，其边缘、纹理和形状信息容易在特征提取过程中被背景响应淹没。现有检测方法通常通过多层卷积和逐级下采样获得高层语义特征，但这一过程会快速降低特征图空间分辨率，使微小目标的局部结构和像素级细节被削弱甚至丢失。虽然特征金字塔和注意力机制能够在一定程度上缓解尺度变化和背景干扰问题，但多数方法更侧重多尺度语义融合，对下采样阶段造成的细粒度空间信息衰减缺乏直接约束；同时，通道特征之间的协同建模不足，使分散在不同通道中的微小目标关键信息难以形成稳定的判别表达。 针对上述问题，提出一种基于重标定细粒度下采样和组间协同建模的微小目标检测网络RFINet。首先，构建重标定细粒度下采样（Recalibrated Fine-grained Downsampling，RFD）模块，以空间切片和通道重组替代传统下采样操作，将局部空间信息映射到通道维度，在降低特征图尺寸的同时保留微小目标的细粒度结构。针对通道扩展带来的冗余响应和背景噪声，RFD进一步引入跨通道自适应重标定机制，根据聚合特征的通道响应强度重新分配权重，从而增强目标相关通道并抑制无关背景。与直接采用步长卷积或池化操作相比，RFD并不简单丢弃空间采样点，而是通过结构化重排将原本容易被压缩的信息转移至通道域，为后续特征提取提供更完整的局部上下文。其次，设计组间协同建模（Inter-group Collaborative Modeling，ICM）模块，将输入特征划分为多个通道子空间，分别提取各组内的局部显著信息，并通过权重再分配、残差连接和通道拼接促进组内增强与组间交互。该设计既保留了分组建模对局部特征的细粒度刻画能力，又缓解了固定分组导致的信息隔离问题，使网络能够更充分地整合分散在不同通道中的微小目标线索。最后，在检测头中完成目标类别预测和边界框回归，实现复杂低空场景下的微小目标检测。 为验证所提模块的作用，本文从不同输入分辨率、模块消融、模型复杂度、检测精度以及跨数据集泛化等角度进行实验分析，以考察RFINet在微小目标密集分布、尺度变化明显和背景干扰较强条件下的综合性能。在VisDrone2019数据集上的实验表明，RFD有效保留细粒度信息并抑制通道扩增带来的背景噪声，ICM则通过组间交互强化关键区域响应，削弱无关背景干扰，二者结合后模型在不同输入分辨率下均取得稳定增益。RFINet在640×640和960×960输入尺寸下的mAP@0.5分别达到40.8%和47.5%，优于参与比较的主流两阶段检测器、单阶段检测器和轻量化检测模型。实验结果表明，所提方法能够在保持较高检测精度的同时增强对密集、遮挡和复杂背景中微小目标的表征能力，为低空无人机视觉感知任务提供了一种更具针对性的特征建模方案。