针对复杂背景下，无人机航拍容易遇到目标小、遮挡严重、检测精度低的问题，提出了一种改进YOLOv5s的无人机航拍小目标检测算法：首先，在YOLOv5s模型的BackBone中加入了可变形卷积C3_DCN，以便获得更好的感受野适应性、特征表达能力和位置精度；其次，在模型的Neck中添加P2检测层，增加了一个高分辨率特征图，以提取到更多的特征信息，从而提升小目标物体的检测能力；之后，再次添加EMA注意力机制以抑制参数的抖动、改善模型的泛化能力及加速模型收敛；最后，使用Focal-EIoU损失函数来改善边界框回归的准确性及提升模型在处理困难样本和应对类别不平衡数据方面的性能。数据集VisDrone2019上的实验结果显示，改进算法的mAP@0.5、mAP@0.95、F₁分别提升了4.6%、3.1%、2.9%，能有效提升无人机航拍图像漏检和误检的状况。

Aiming at the challenges encountered in aerial drone photography under complex backgrounds, such as small targets, severe occlusions, and low detection accuracy, this paper proposes an improved YOLOv5s-based algorithm for detecting small targets in drone-captured images. Firstly, deformable convolution C3_DCN was integrated into the Backbone of the YOLOv5s model to achieve enhanced receptive field adaptability, feature representation capabilities, and positional accuracy. Secondly, a P2 detection layer was added to the Neck of the model, introducing a high-resolution feature map that extracts additional feature information, thereby improving the detection capability for small targets. Furthermore, an EMA (exponential moving average) attention mechanism was incorporated to suppress parameter oscillations, enhance the model's generalization ability, and accelerate convergence. Finally, the Focal-EIoU loss function was employed to improve the accuracy of bounding box regression and boost the model's performance on challenging samples and class-imbalanced datasets. Experimental results on the VisDrone2019 dataset demonstrate that the improved algorithm has achieved increases of 4.6%, 3.1%, and 2.9% in mAP@0.5, mAP@0.95, and F₁, respectively, significantly reducing the rates of missed detections and false positives in aerial drone imagery.