南京邮电大学学报（自然科学版）2026，Vol.46Issue(3)：132-139,8.DOI:10.14132/j.cnki.1673-5439.2026.03.014
基于YOLO的无人机小目标高精度检测改进模型
An improved high-precision model for UAV small object detection based on YOLO
摘要
Abstract
As a fundamental technology for multi-domain research,target detection in unmanned aerial vehicle(UAV)imagery has been widely applied to various complex scenarios.However,aerial images of-ten contain small,densely distributed objects against complex backgrounds,leading to challenges such as missed detections,false positives,and inaccurate localization in existing models.To improve detec-tion performance,this paper proposes a YOLO-based high-precision detection model for small UAV ob-jects.First,a selective convolution block(SCB)is adopted to replace the original cross-stage module,reducing computational complexity.Second,a small target scale sequence fusion(STSSF)structure is designed to enhance multi-scale feature fusion.Third,a shared convolution precision detection(SCPD)module is proposed to improve feature extraction efficiency and cross-scale feature consistency.Experi-mental results on the VisDrone2019-DET dataset show that YOLO-LiteMax achieves a mAP@0.5 of 45.2%,which is 5.9%higher than that of YOLOv8,along with a 3.8%increase in APsmall.These re-sults demonstrate superior precision and practical applicability in small-object detection.关键词
无人机/目标检测/特征提取/图像处理Key words
unmanned aerial vehicle(UAV)/object detection/feature extraction/image processing分类
信息技术与安全科学引用本文复制引用
杨畅,苏健,张健..基于YOLO的无人机小目标高精度检测改进模型[J].南京邮电大学学报（自然科学版）,2026,46(3):132-139,8.基金项目
国家自然科学基金(61802196)资助项目 （61802196）
