摘要： 针对低空复杂背景下无人机目标尺寸小、纹理特征弱、易受背景干扰导致检测精度下降及漏检率较高的问题，提出一种基于YOLOv11改进的轻量化无人机目标检测方法EF-YOLO（Efficient Fusion YOLO）。针对传统模型在复杂场景中存在深层语义信息不足、多尺度特征融合失配以及复杂背景干扰严重等问题，对YOLOv11网络结构进行系统优化。首先，在主干网络深层设计C3K2-MSFE模块，通过分组卷积与多尺度卷积核协同建模，增强深层弱目标的判别能力，并利用线性分支保持主语义流稳定，从而提升复杂背景下小目标无人机的特征表达能力。其次，提出改进的多尺度特征融合结构PSG-FPN，在上采样阶段引入软最近邻插值机制，通过软化系数减缓跨尺度特征融合中的语义覆盖问题，同时结合GSConvE优化下采样过程，并新增P2检测层以增强模型对小尺度无人机目标的感知能力，从而改善多尺度特征对齐效果。随后，针对传统检测头参数冗余与计算开销较大的问题，提出共享特征轻量化检测头SFE-Head，采用共享卷积主干与分组卷积结构实现分类与回归特征共享，在保持检测精度的同时显著降低模型参数量与计算复杂度。最后，对YOLOv11中的C2PSA模块进行改进，引入基于频域筛选机制的前馈增强结构，通过频域特征建模抑制复杂背景中的纹理噪声，提高模型对微弱无人机目标的响应能力与抗干扰性能。实验分别在DUT Anti-UAV数据集与Det-Fly数据集上进行。结果表明，在DUT Anti-UAV数据集上，EF-YOLO的Precision、Recall、mAP50与mAP50-95分别达到96.76%、88.16%、92.59%和62.41%，相比原始YOLOv11分别提升4.01%、3.44%、3.04%和6.18%；模型参数量由2.58M降低至2.27M，模型大小由5.3MB减小至4.8MB，在较低计算复杂度条件下仍保持良好的实时检测性能。在Det-Fly数据集上，EF-YOLO的mAP50和mAP50-95分别达到96.4%和68.06%，相比YOLOv11分别提升2.39%和2.16%，验证了模型在不同复杂低空场景中的泛化能力与稳定性。与此同时，消融实验结果表明，各改进模块在复杂背景下具有良好的协同增益作用，其中C3K2-MSFE能够有效增强深层弱目标特征表达，PSG-FPN能够改善跨尺度语义对齐能力，SFE-Head在降低模型复杂度的同时维持检测精度，而频域增强结构则进一步提升了模型对复杂纹理噪声的抑制能力。相比传统目标检测模型，EF-YOLO在复杂背景、远距离目标以及低对比度场景下具有更稳定的检测表现，能够有效减少漏检与误检现象，并在保持模型轻量化的同时增强特征表达能力。综合实验结果可以看出，EF-YOLO在检测精度、模型轻量化与实时性之间取得了较好的平衡，不仅能够有效提升复杂低空环境下无人机目标的检测能力，同时也具备较强的工程部署潜力，可为低空空域监管、边缘智能监测及无人机安全防控等应用场景提供可靠的技术支撑。

Abstract: To address the problems of low detection accuracy and high missed detection rate caused by the small size,weak texture features,and severe background interference of unmanned aerial vehicle (UAV) targets in low-altitude complex environments,a lightweight UAV object detection method named EF-YOLO (Efficient Fusion YOLO) based on YOLOv11 is proposed. Considering the limitations of existing models in complex scenarios,including insufficient deep semantic representation, multi-scale feature fusion mismatch, and serious background interference, the YOLOv11 network architecture is systematically optimized. First, a C3K2-MSFE module is introduced into the deep layers of the backbone network. By combining grouped convolution with multi-scale convolution kernels, the module enhances the discriminative ability of weak deep target features while maintaining the stability of the main semantic flow through a linear branch, thereby improving the feature representation capability of small UAV targets in complex backgrounds. Second, an improved multi-scale feature fusion structure named PSG-FPN is proposed. A Soft Nearest Neighbor Interpolation mechanism is adopted during the upsampling stage to alleviate semantic coverage problems in cross-scale feature fusion through adaptive soft coefficients. Meanwhile,GSConvE is employed to optimize the downsampling process,and an additional P2 detection layer is introduced to enhance the perception ability for small-scale UAV targets, thereby improving multi-scale feature alignment. Furthermore, to reduce parameter redundancy and computational overhead in traditional detection heads, a lightweight shared-feature detection head named SFE-Head is designed. By adopting a shared convolutional stem and grouped convolution structure, classification and regression branches can share features,which significantly reduces model parameters and computational complexity while maintaining detection accuracy. Finally,the original C2PSA module in YOLOv11 is improved by introducing a frequency-domain enhanced feedforward structure. Through frequency-domain feature modeling, redundant texture noise in complex backgrounds can be effectively suppressed, thereby improving the response capability and anti-interference performance for weak UAV targets. Experiments are conducted on the DUT Anti-UAV and Det-Fly datasets. The results show that, on the DUT Anti-UAV dataset, EF-YOLO achieves Precision, Recall, mAP50, and mAP50-95 values of 96.76%, 88.16%, 92.59%, and 62.41%, respectively, representing improvements of 4.01%, 3.44%, 3.04%, and 6.18% compared with the original YOLOv11. Meanwhile, the number of model parameters is reduced from 2.58M to 2.27M, and the model size decreases from 5.3 MB to 4.8 MB, while maintaining favorable real-time detection performance under relatively low computational complexity. On the Det-Fly dataset, EF-YOLO achieves mAP50 and mAP50-95 values of 96.4% and 68.06%, respectively, which are 2.39% and 2.16% higher than those of YOLOv11, demonstrating excellent generalization ability and stability under different low-altitude complex scenarios. In addition, ablation experiments verify that the proposed modules provide significant collaborative benefits. Specifically, the C3K2-MSFE module effectively enhances weak deep feature representation, PSG-FPN improves cross-scale semantic alignment, SFE-Head reduces model complexity while maintaining detection accuracy, and the frequency-domain enhancement structure further suppresses complex texture noise. Compared with conventional object detection models, EF-YOLO exhibits more stable detection performance in complex backgrounds, long-distance target scenarios, and low-contrast environments, effectively reducing missed detections and false detections while preserving model lightweight characteristics. Overall, EF-YOLO achieves a favorable balance among detection accuracy, lightweight design, and real-time performance, showing strong engineering deployment potential and providing reliable technical support for low-altitude airspace supervision, edge intelligent monitoring, and UAV security applications.