无人机产业的快速发展，使得非法飞行活动日益增多，对公共安全构成了潜在威胁。因此，对无人机的快速且准确的目标检测成为了至关重要的任务。鉴于现有的基于深度学习的目标检测方法在检测精度与速度间权衡不足，以及模型体积过大，难以适配边缘检测设备等问题，提出基于改进YOLOv8n的小型无人机检测模型YOLO-Drone。首先，将主干网络中的C2f模块替换为MobileNetv3网络中的bneck模块，以实现更轻量化的特征提取，同时，部分卷积操作采用PConv的模式，减少冗余计算；其次，为了更好保留无人机的细节特征，在主干网络中引入SPD-Conv来实现下采样，增强了模型对小目标的识别能力；然后，移除大目标检测头，从而专注于对小目标的检测，并缩减网络宽度以降低模型复杂度；最后，引用WIoU作为边界框回归损失函数，提升目标位置预测的精度。基于公开数据集的实验结果表明：改进后的模型mAP@0.5达到了94.1%，相比YOLOv8n提升了0.8%，浮点运算量和参数量分别减少了40%、70%。该模型在进一步轻量化的同时兼顾了检测精度，为边缘设备部署提供了有效参考。

With the rapid growth of the drone industry, the number of illegal flights is on the rise, threatening public safety. Thus, the task of efficient and accurate drone detection is crucial. In response to the limitations of existing deep learning-based object detection methods, such as the insufficient balance between accuracy and speed, and model sizes being too large to be suitable for edge devices, an improved unmanned aerial vehicle(UAV) target detection method called YOLO-Drone was proposed based on YOLOV8n. Firstly, the C2f module in the backbone was replaced with the bneck module from MobileNetv3 to achieve more lightweight feature extraction, meanwhile, the PConv mode was used for some convolution operations to reduce redundant calculations. Secondly, aiming to better retain the detailed features of drones and enhance the model's ability to recognize small targets, SPD-Conv was introduced into the backbone network for downsampling. Thirdly, focusing on the detection of small targets, the large target detection head was removed, and the network width was reduced to lower the model complexity. Lastly, WIoU was used as the loss function to improve the accuracy of localization. The experimental results based on public datasets show that the proposed model achieves an mAP@0.5 of 94.1%, which is 0.8% higher than that of YOLOv8n. Meanwhile, the computational load and the number of parameters are reduced by 40% and 70% respectively. While achieving further lightweight design and maintaining detection accuracy, it makes the proposed model a more suitable option to be deployed in the edge computing devices.