基于YOLOv8的无人机编队领航者检测算法

UAV Formation Leader Detection Algorithm Based on YOLOv8

基于视觉的无人机编队方法具有不受通信拒止影响的优点,与传统编队算法相比有更强的鲁棒性,逐渐成为了领域内的研究热点。在Leader-Follower无人机视觉编队模式中,跟随者通过对领航者执行实时目标检测,并解算出领航跟随者之间的相对位置关系来完成编队控制任务。基于YOLOv8n目标检测模型提出了一种改进的实时目标检测算法:在Neck模块中加入可变形卷积模块;加入多头注意力机制增强特征提取;在训练过程中进行数据增强。为验证所提算法的性能优势,进行了2次对比测试,实验结果表明,改进算法比原始算法的特征提取效果更强,检测精度更高。最后,将改进的领航者检测算法应用于无人机编队任务中,证明了所提算法的实际应用价值。

The vision-based unmanned aerial vehicle(UAV) formation method has the advantage of being unaffected by communication disruptions and exhibits greater robustness compared to traditional formation algorithms,gradually becoming a research hotspot in the field.In the Leader-Follower UAV visual formation mode,followers achieve formation control by performing real-time target detection on the leader and calculating the relative positional relationship between the leader and the followers.This paper proposes an improved real-time object detection algorithm based on the YOLOv8n object detection model:convolution modules were added in the Neck module,a multi-head attention mechanism was added to enhance feature extraction,apply data augmentation was applied in the training process.To validate the performance advantages of the algorithm proposed,two comparative tests were conducted.The experimental results indicate that the improved algorithm exhibits stronger feature extraction and higher detection accuracy compared to the original algorithm.Finally,the improved object detection algorithm is applied to drone formation tasks,demonstrating the practical utility of the algorithm in this context.