Abstract: To address the severe challenges in Unmanned Aerial Vehicle (UAV) aerial imagery, including drastic target scale variations, feature degradation caused by low pixel coverage of small targets, and intense background noise interference, a Multi-attention Enhanced Recursive Feedback Detector (MERF-DETR) is proposed, utilizing the real-time end-to-end detector RT-DETR-L as the baseline framework. First, a Multi-Scale Triplet Attention (MSTA) module is designed at the end of the backbone network, using a partial channel split strategy to perform parallel depthwise separable convolutions with varying kernel sizes exclusively on key feature channels, establishing multi-scale local spatial representations while controlling parameter redundancy, and combining cross-dimensional triplet attention to enhance feature interaction between spatial and channel dimensions for improved multi-scale target representation. Second, a Neck Recursive Feedback Feature Pyramid Network (NRF-FPN) is constructed with local recursive feedback paths, utilizing deep semantic features for top-down semantic calibration of shallow high-resolution features to alleviate small target feature degradation during progressive downsampling. Finally, to combat background noise such as complex textures and shadows, a Coordinate guided Spatial frequency complementary Feature Enhancement (CSFE) module is introduced, leveraging coordinate attention mechanisms to focus on spatial directional information, depthwise convolutions to compensate for local edge textures, and 2D real Fast Fourier Transform-based frequency-domain filtering to model global context for complementary feature enhancement. Experimental results demonstrate that MERF-DETR achieves mAP@50 scores of 51.3% and 47.4% on the VisDrone2019 and UAVDT datasets, representing improvements of 9.1 and 5.8 percentage points over Deformable-DETR, respectively. Ablation studies indicate that individually incorporating the MSTA, NRF-FPN, and CSFE modules improves mAP@50 over the baseline by 1.5, 3.6, and 1.1 percentage points, respectively. Meanwhile, the CSFE module effectively mitigates the computational redundancy introduced by NRF-FPN, and the synergy of all three modules achieves optimal overall performance, significantly enhancing small target detection accuracy in drone aerial scenarios.

摘要： 针对无人机航拍图像中目标尺度变化大、小目标像素占比低导致的特征退化以及复杂背景干扰严重等问题，提出一种结合多注意力特征增强与递归反馈的无人机小目标检测方法（Multi-attention Enhanced Recursive Feedback Detector, MERF-DETR）。该方法以端到端检测器RT-DETR-L为基准架构。首先，在骨干网络末端设计多尺度三维注意力模块（Multi-Scale Triplet Attention, MSTA），采用部分通道分流策略，仅对关键特征执行不同核尺寸的并行深度可分离卷积，在控制参数冗余的同时建立多尺度局部空间表征，并结合跨维度三重注意力增强空间与通道维度间的特征交互，提升不同尺度目标的特征表达能力。其次，设计颈部递归反馈特征金字塔（Neck Recursive Feedback Feature Pyramid Network, NRF-FPN），通过构建局部递归反馈路径，利用深层语义特征对浅层高分辨率特征进行自顶向下的语义校准，缓解逐级下采样过程中小目标特征退化的问题。最后，针对复杂纹理与阴影等背景噪声对小目标检测的干扰，提出坐标引导空间与频域互补特征增强模块（Coordinate guided Spatial frequency complementary Feature Enhancement, CSFE），通过坐标注意力机制聚焦关键目标的空间方向信息，利用深度卷积补偿局部边缘纹理细节，同时引入基于二维实数快速傅里叶变换的频域滤波建模全局上下文关系，实现空间与频域互补的特征增强。实验结果表明，MERF-DETR在VisDrone2019和UAVDT数据集上的mAP@50分别达到51.3%与47.4%，较Deformable-DETR分别提升了9.1与5.8个百分点。消融实验表明，单独引入MSTA、NRF-FPN和CSFE模块后，mAP@50较基准分别提升1.5、3.6和1.1个百分点；同时CSFE能够有效抑制NRF-FPN引入的计算冗余，三个模块协同作用实现了最佳综合性能，有效提升了无人机航拍场景下的小目标检测精度。