计算机工程与应用2025，Vol.61Issue(21)：105-116,12.DOI:10.3778/j.issn.1002-8331.2504-0095
改进YOLOv8n的无人机对地多尺度目标检测算法及实现
Improved UAV Multi-Scale Object Detection Algorithm Based on YOLOv8n for Ground Tar-gets and Implementation
摘要
Abstract
In view of the challenges in target detection from an unmanned aerial vehicle(UAV)perspective,such as small target sizes,large scale variations,and mutual occlusion in complex scenarios,an improved target detection network based on YOLOv8n is proposed.the spatial depth convolution(SPD-Conv)is introduced and lightweight improvements are made to it,resulting in the SPDs-Conv.This not only enhances the performance of small target detection but also effec-tively reduces the number of model parameters.The C2f_SKAttention module is designed to dynamically adjust multi-scale information,achieving better information fusion.A novel dynamic detection head strategy is adopted.This strategy organically unifies the detection heads for various types of targets,reducing the impact of occlusion and other issues caused by complex scenarios on detection.Experiments on the VisDrone2019 dataset show that the improved model achieves an average precision of 49.7%with 6.9×106 parameters,which is 15.1 percentage points higher than the baseline algorithm.It also has an inference speed of 41 FPS,enabling real-time detection.Compared with other algorithms,the proposed algorithm can achieve higher detection accuracy.The improved model also achieves an average precision of 49.3%on the Jetson AGX Orin device,with an inference speed of 28.7 FPS,verifying its efficiency and applicability to real-time object detection tasks on embedded edge platforms.关键词
无人机/目标检测/SPDs-Conv/C2f_SKAttention模块/动态检测头Key words
unmanned aerial vehicle(UAV)/object detection/SPDs-Conv/C2f_SKAttention module/dynamic detection head分类
计算机与自动化引用本文复制引用
吴思,黄丹丹,刘智,王惠绩,田成军..改进YOLOv8n的无人机对地多尺度目标检测算法及实现[J].计算机工程与应用,2025,61(21):105-116,12.基金项目
国家重大科研仪器研制项目(62127813). （62127813）
