计算机工程2026，Vol.52Issue(3)：97-106,10.DOI:10.19678/j.issn.1000-3428.0070085
基于改进YOLOv8的轻量化无人机图像目标检测算法
Lightweight Target Detection Algorithm for UAV Images Based on Improved YOLOv8
摘要
Abstract
In view of missed and false detection phenomena caused by numerous small target instances and occlusions among targets in drone images,this paper proposes a lightweight small target detection algorithm for Unmanned Aerial Vehicle(UAV)images based on an improved YOLOv8.The Triple Feature Encoder(TFE)and Scale Sequence Feature Fusion(SSFF)modules are introduced in the neck to enhance the ability of the network to extract features at different scales.Furthermore,a Small Object Detection Head(SMOH)is designed and fused with the improved neck feature extraction network,and an additional detection head is also introduced to reduce the loss of small target features and enhance the recognition ability of the network for small targets.Additionally,considering the defects of Complete Intersection over Union(CIoU),a regression loss function,Wise-Inner-MPDIoU,is proposed by combining Wise-IoU,Inner-IoU,and Minimum Point Distance based IoU(MPDIoU).Finally,to realize the lightweight application requirements of the algorithm in mobile and embedded systems,amplitude-based layer-adaptive sparse pruning is performed to further reduce the model size while ensuring model accuracy.Experimental results demonstrate that,compared to the original YOLOv8s model,the improved model proposed in this paper improves mAP@0.5 by 6.8 percentage points,while reducing the number of parameters,amount of computation,and model size by 76.4％,17.1％,and 73.5％,respectively.The proposed model is lightweight,improves detection accuracy,and has strong practical significance.关键词
YOLOv8算法/无人机/小目标检测/特征融合/模型剪枝Key words
YOLOv8 algorithm/Unmanned Aerial Vehicle(UAV)/small target detection/feature fusion/model pruning分类
信息技术与安全科学引用本文复制引用
唐克,魏飞鸣,李东瀛,郁文贤..基于改进YOLOv8的轻量化无人机图像目标检测算法[J].计算机工程,2026,52(3):97-106,10.基金项目
上海航天先进技术联合研究基金(USCAST2022-32). （USCAST2022-32）
