红外技术2026，Vol.48Issue(4)：484-493,10.
基于YOLO v8的航拍图像小目标检测算法
Small-Target Detection Algorithm for Aerial Images Based on YOLO v8
摘要
Abstract
Aiming at the challenges of low resolution,high target density,and background similarity in aerial small-target detection,this study proposes an improved detection algorithm,TINY-YOLOv8.First,to enhance the detection capability for small targets,an additional small-target detection layer is incorporated into the YOLOv8 model,and the backbone network structure is optimized to improve detection accuracy.Second,partial convolutions in the backbone network are replaced with lightweight convolutions,and the C2f module in the neck is substituted with a lightweight VoV-GSCSP module to reduce model parameters and computational complexity.Furthermore,the ECA attention mechanism is integrated into the network to strengthen channel-wise feature interactions,enabling the detection head to extract more delicate target features.Finally,an Inner-MPDIoU loss function is designed to accelerate model convergence and enhance target localization accuracy.Experimental results show that the proposed TINY-YOLOv8 algorithm outperforms other target detection methods.Compared with the benchmark model,the proposed approach improves mAP50 by 5.2%and 8%on the VisDrone and TinyPerson datasets,respectively,while reducing the number of parameters by 10.1%.These results indicate that the proposed algorithm is well-suited for aerial image target detection applications.关键词
YOLOv8/航拍图像/小目标检测/VoV-GSCSP/ECA/Inner-MPDIoUKey words
YOLOv8/aerial image/small target detection/VoV-GSCSP/ECA/Inner-MPDIoU分类
信息技术与安全科学引用本文复制引用
张上,院永莹,陈永麟,王莹,崔玉杰..基于YOLO v8的航拍图像小目标检测算法[J].红外技术,2026,48(4):484-493,10.基金项目
国家级大学生创新创业训练计划(202011075013),国家级大学生创新创业训练计划(202111075012). （202011075013）
