LFEA-YOLO for Small-Object Detection in UAV Aerial Images

In recent years, object detection using aerial images captured by unmanned aerial vehicles (UAVs) has become a research hotspot. However, due to the high resolution of UAV imagery, large variations in object scales, and the predominance of small targets, achieving fast and accurate object classification remains a significant challenge. To address these issues, this paper proposes a lightweight feature enhancement attention network, termed LFEA-YOLO. First, a multi-scale feature fusion (MSFF) module is introduced, which establishes a cross-scale feature interaction mechanism to effectively integrate semantic information with spatial details while reducing the computational overhead caused by redundant features. Second, deformable convolution networks (DCNs) are incorporated into the neck network to construct the DCNv4-C2F module. Leveraging its adaptive spatial sampling capability, this module dynamically adjusts the receptive field of convolution kernels, thereby overcoming the limitations of traditional fixed-grid sampling and enhancing the network’s ability to model geometric variations of small-scale objects. Finally, an attention-enhanced detection head (AE-Head) is designed, integrating a large separable kernel attention (LSKA) mechanism to dynamically emphasize discriminative features in key target regions. Experimental results on the VisDrone2019 and DOTAv1.0 datasets demonstrate significant improvements over the baseline model, with mAP50 and mAP50:95 increasing by 10.6% and 7.8%, respectively. Compared with several recently proposed models, LFEA-YOLO also exhibits clear advantages, validating the effectiveness of the proposed approach.