改进YOLOv8的轻量化无人机目标检测算法

Improved YOLOv8 Lightweight UAV Target Detection Algorithm

针对无人机目标检测算法计算复杂难以部署,且长尾分布的无人机数据导致检测精度较低的问题,提出了基于改进YOLOv8的轻量化无人机目标检测算法(PC-YOLOv8-n),可均衡网络检测精度与计算量,并对长尾分布数据有一定泛化能力。使用部分卷积层(PConv)替换YOLOv8中的3×3卷积层,对网络进行轻量化处理,解决网络冗余和计算量复杂的问题;融合双通道特征金字塔,增加自上而下的路径,将深层信息与浅层信息进行融合,同层引入轻量化注意力机制,提升网络的特征提取能力;采用均衡焦点损失(EFL)作为类别损失函数,通过均衡尾部类别在网络训练时的梯度权重,增加网络的类别检测能力。实验结果表明,PC-YOLOv8-n在VisDrone2019数据集中具有良好的表现,在mAP50精度上比原始YOLOv8-n算法提高了1.6个百分点,同时模型的参数和计算量分别降低为2.6×10^(6)和7.6 GFLOPs,检测速度达到77.2 FPS。

Aiming at the problem that UAV target detection algorithms are computationally complex and difficult to deploy,and the long-tailed distribution of UAV data leads to low detection accuracy,a lightweight UAV target detection algorithm based on improved YOLOv8(PC-YOLOv8-n)is proposed,which can balance the network detection accuracy and computation,and has some generalisation ability to long-tailed distribution of data.Using partial convolutional layers(PConv)to replace the 3×3 convolutional layers in YOLOv8,the network is lightweighted to solve the problems of net-work redundancy and computational complexity;it fuses dual-channel feature pyramids,increases top-down paths,fusion of deep and shallow information,and introduces a lightweight attention mechanism in the same layer to improve the feature extraction ability of the network;it uses the equilibrium focus loss(EFL)as the category loss function to increase the category detection ability of the network by equalising the gradient weights of the tail categories during net-work training.The experimental results show that PC-YOLOv8-n has good performance in the VisDrone2019 dataset,improving 1.6 percentage points in mAP50 accuracy over the original YOLOv8-n algorithm,while the parameters and com-putation of the model are reduced to 2.6×10^(6)and 7.6 GFLOPs,respectively,and the detection speed reaches 77.2 FPS.