基于改进YOLOv8s的无人机航拍目标检测算法

A UAV Aerial Target Detection Algorithm Based on Improved YOLOv8s

针对无人机航拍图像存在目标小、目标遮挡等问题,提出一种改进YOLOv8s的目标检测算法YOLO-RC。在骨干网络结构中引入感受野空间注意力(RFA),避免卷积核参数共享,以提高模型的图像特征提取性能。改进C2f模块,引入深度分离卷积,减少模型的计算量。新增混合注意力卷积的小目标检测层,以改善对小目标检测精度。为充分考虑预测图像几何特征,使用MPDIoU损失函数优化网络。在无人机图像数据集VisDrone2019上的实验表明,所提改进算法的mAP@0.5为44.7%,较YOLOv8s提升了5.4个百分点,在新增小目标检测层的情况下,参数量降低了1.81×106。在DOTAv1.0数据集上,mAP@0.5提高了5.6个百分点。改进后的算法具有更强的鲁棒性,适用于无人机视角目标检测任务。

In order to solve the problems of small targets and target occlusion in UAV aerial images,an improved target detection algorithm YOLO-RC based on YOLOv8s is proposed.The Receptive-Field spatial Attention(RFA)is introduced into the backbone network structure to avoid the sharing of convolutional kernel parameters,so as to improve the image feature extraction performance of the model.The C2f module is improved and deep separation convolution is introduced to reduce the computational cost of the model.A small object detection layer of hybrid attention convolution is added to improve the detection accuracy of small objects.In order to fully consider the geometric features of the predicted image,the MPDIoU loss function is used to optimize the network.Experiments on the UAV image dataset VisDrone2019 show that the mAP@0.5 of the proposed improved algorithm is 44.7%,which is 5.4 percentage points higher than that of YOLOv8s,and the number of parameters is reduced by 1.81×106 with the addition of a small target detection layer.On the DOTAv1.0 dataset,the mAP@0.5 increased by 5.6 percentage points.The improved algorithm has stronger robustness and is suitable for UAV perspective target detection tasks.