电讯技术2025，Vol.65Issue(11)：1773-1780,8.DOI:10.20079/j.issn.1001-893x.240527001
基于多尺度特征的无人机目标识别算法
UAV Target Recognition Based on Multi-scale Features
摘要
Abstract
In view of the problems of false detection and missed detection when an unmanned aerial vehicle(UAV)detects targets at different scales,a YOLOv8-FDT UAV algorithm model with a multi-scale fusion mechanism is proposed.First,a dynamic upsampling module is added to the Neck layer of the baseline model to reduce the number of model parameters and improve the real-time performance of the model for target recognition.In addition,in order to enable the entire algorithm model to capture different scale semantic information of the target in the feature fusion stage,adaptive downsampling and depth convolution are integrated to design the feature diffusion pyramid network(FDPN).Finally,experiments on the UAV aerial photography dataset VisDrone2019 show that the mean average precision(mAP)of all categories of the improved model is increased by 6.24%compared with that of the baseline model.关键词
无人机/目标识别/特征聚焦/多尺度融合Key words
UAV/small target recognition/focus feature/multi-scale fusion分类
计算机与自动化引用本文复制引用
张博文,薛波..基于多尺度特征的无人机目标识别算法[J].电讯技术,2025,65(11):1773-1780,8.基金项目
国家自然科学基金资助项目(62003151) （62003151）
