针对无人机航拍图像中存在的背景复杂、目标遮挡、小目标聚集及特征不明显等问题，提出了一种基于改进YOLOv8n的无人机航拍小目标检测算法CSP-YOLO（cross stage partial-you only look once）。首先，设计了空间到深度卷积（space-to-depth convolution, SPD-Conv）模块，以减轻小目标特征信息的丢失，并增强特征信息的鲁棒性。其次，基于残差技术提出了C-SimAM（convolution simple attention module）注意机制，提升复杂背景下低分辨率特征识别的精度。为增强对密集小目标的检测能力，重新设计了颈部结构，并加入一个专注于捕获浅层特征图中的细粒度信息的微型检测头（tiny prediction head），优化对密集小目标的聚焦能力。最后，为应对正负样本不均衡的问题，引入WIoU（wise intersection over union）损失函数，以加速模型收敛，并提升边界框回归的精度。实验结果显示，CSP-YOLO算法在Visdrone2019数据集上表现优异，其平均精度mAP0.5达到0.405，相比YOLOv8n，性能相对提升了约23.1%。相较于当前主流的目标检测方法以及YOLO系列算法，CSP-YOLO不仅展现了出色的性能和稳定性，还实现了模型检测精度和模型大小的平衡，适用于无人机视角目标检测任务，具备较高的实用价值和广泛的应用前景。

To address challenges in drone aerial imagery such as complex backgrounds, occlusions, dense small objects, and indistinct features, a small object detection algorithm based on an improved YOLOv8n, named CSP-YOLO (cross stage partial-you only look once), was proposed. First, the space-to-depth convolution (SPD-Conv) module was designed to reduce the loss of feature information for small objects and enhance feature robustness. Second, the C-SimAM (convolution simple attention module) attention mechanism, based on residual techniques, was proposed to improve the accuracy of low-resolution feature recognition in complex backgrounds. To enhance the detection capability for dense small objects, the neck structure was redesigned with the addition of a tiny prediction head, which focused on capturing fine-grained information from shallow feature maps to optimize the ability to focus on dense small objects. Finally, to tackle the imbalance between positive and negative samples, the wise intersection over union (WIoU) loss function was introduced to accelerate model convergence and improve the precision of bounding box regression. Experimental results show that the CSP-YOLO algorithm performs excellently on the VisDrone2019 dataset, achieving an mAP0.5 of 0.405, which represents a relative performance improvement of approximately 23.1% compared to YOLOv8n. Compared to mainstream object detection methods and algorithms in the YOLO series, CSP-YOLO not only demonstrates outstanding performance and stability but also achieves a balance between detection accuracy and model size. This makes it suitable for drone-perspective object detection tasks and proves its significant practical value and broad application prospects.