计算机工程与应用2025，Vol.61Issue(11)：119-131,13.DOI:10.3778/j.issn.1002-8331.2411-0459
改进YOLOv8的无人机航拍小目标检测算法
Improved YOLOv8 Algorithm for Small Target Detection in Drone Aerial Photography
摘要
Abstract
Detecting small and densely distributed targets in UAV aerial images poses challenges such as complex back-grounds,imbalanced sample numbers,and limited computational power.To address these issues,an improved YOLOv8 algorithm,MFF-YOLOv8(multi-feature fusion YOLOv8),is proposed.This algorithm integrates deformable convolution DCNv3(deformable convolution v3)into the Bottleneck module of the C2f module to enhance feature extraction.A new MFFPN(multi-feature fusion pyramid network)is designed to add more feature fusion routes,retain low-level feature map details,and improve small target detection.Additionally,a P2 small target detection layer is added and the P5 detec-tion layer is optimized to enhance accuracy and reduce parameters.The introduction of the Dyhead(dynamic head)fur-ther improves detection precision of the model.In the experiment on the Visdrone2019 dataset,MFF-YOLOv8s has a 10.2 percentage points and 7.1 percentage points increase in mAP50 and mAP50:95 respectively compared to YOLOv8s,with a 77.04%reduction in parameters.The model's detection accuracy surpasses that of YOLOv11,meeting the preci-sion and lightweight requirements for UAV platforms.关键词
YOLOv8/小目标检测/多尺度特征融合/轻量化Key words
YOLOv8/small targets detection/multi-scale feature fusion/lightweight分类
计算机与自动化引用本文复制引用
许景科,索祥龙,周磊..改进YOLOv8的无人机航拍小目标检测算法[J].计算机工程与应用,2025,61(11):119-131,13.基金项目
国家重点研发计划(2020YFC0833203). （2020YFC0833203）
