计算机工程与应用2024，Vol.60Issue(8)：182-191,10.DOI:10.3778/j.issn.1002-8331.2310-0063
改进YOLOv8的轻量化无人机目标检测算法
Improved YOLOv8 Lightweight UAV Target Detection Algorithm
摘要
Abstract
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 2.6×106 and 7.6 GFLOPs,respectively,and the detection speed reaches 77.2 FPS.关键词
无人机/YOLOv8/长尾分布/目标检测/部分卷积Key words
unmanned aerial vehicle(UAV)/YOLOv8/long-tail distribution/object detection/partial convolution分类
信息技术与安全科学引用本文复制引用
胡峻峰,李柏聪,朱昊,黄晓文..改进YOLOv8的轻量化无人机目标检测算法[J].计算机工程与应用,2024,60(8):182-191,10.基金项目
中央高校基本科研任务专项资金(2572019BF09). （2572019BF09）
