高技术通讯2026，Vol.36Issue(6)：593-601,9.DOI:10.3772/j.issn.1002-0470.2026.06.005
基于YOLOv8n的航拍地面目标检测改进算法
Improved algorithm for aerial ground target detection based on YOLOv8n
摘要
Abstract
To address the poor performance of traditional object detection algorithms in small object detection for aerial images—attributed to challenges like low target resolution and complex backgrounds—this study proposes three key improvements based on the YOLOv8n(you only look once version 8 nano)model.First,the Swin Transformer module and multi-scale feature fusion technology are embedded into the original C2f(conv 2 fusion)module,form-ing an enhanced feature extraction unit that strengthens the model's capability to capture fine-grained features of objects across different scales,particularly small ones.Second,a square distance intersection over union(SDIoU)loss function is developed;by integrating an optimization strategy that combines squared diagonal distance and aver-age area ratio,this function effectively reduces bounding box regression errors,enabling more accurate localization of small targets.Third,a multi-dimensional small object detection head is designed to analyze targets from seman-tic,spatial,and scale dimensions,which significantly mitigates the missed detection of small objects in aerial scenes.Comprehensive experiments were conducted on VisDrone 2019,a widely recognized benchmark dataset for aerial object detection.The results demonstrate that the improved model achieves mAP@50 of 0.406 and mAP@50∶95 of 0.241,representing increases of 5.8%and 3.7%,respectively,compared to the original YOLOv8n model.This outcome validates that the proposed improvements effectively balance detection accuracy and efficiency for small object detection in practical aerial scenarios.关键词
YOLOv8n/目标检测/航拍Key words
YOLOv8n/object detection/aerial photography引用本文复制引用
黄成,林生佐,吴建东..基于YOLOv8n的航拍地面目标检测改进算法[J].高技术通讯,2026,36(6):593-601,9.基金项目
广东省2024年度普通高校重点科研平台(2024ZDZX4152)资助项目. （2024ZDZX4152）
