针对无人机小目标检测中漏检率高、检测成功率低等问题，提出一种基于YOLOv5的小目标检测算法。首先，分别在backbone结构和neck结构中，融合swin transformer模块，在减少计算成本的基础上，提高目标检测的准确率，以适应无人机航拍小目标检测；其次，引入卷积注意力模块（convolutional block attention module，CBAM），以增强网络对小目标特征的关注度；最后，将原始损失函数CIoU替换为SIoU损失函数，强调高质量样本权重加速收敛，提高回归精度。实验结果表明，经过模型优化，在Visdrone2019数据集上的检测精度为35.3%，与YOLOv5相比，提升了5.2%；相较于其他经典及先进算法，SWCBSI-YOLO算法表现良好，满足针对无人机航拍小目标的检测要求。

In order to solve the problems of high missed detection rate and low detection success rate in UAV small target detection， a small target detection algorithm based on YOLOv5 was proposed.Firstly， the swin transformer module was integrated into the backbone structure and the neck structure respectively， which improved the accuracy of target detection on the basis of reducing the computational cost， and could adapt to the detection of small target in UAV aerial photography.Secondly， the convolutional block attention module （CBAM） was introduced to enhance the network’s attention for small target features.Finally， the original loss function CIoU was replaced by the SIoU loss function， and the weights of high-quality samples were emphasized to accelerate convergence and improve the regression accuracy.Experimental results show that the detection accuracy on Visdrone2019 dataset is 35.3% after model optimization， which is 5.2% higher than that of YOLOv5.Compared with other classical and advanced algorithms，SWCBSI-YOLO algorithm performs well and meets the detection requirements of small targets for UAV aerial photography.