红外技术2026，Vol.48Issue(4)：476-483,8.
基于空间特征融合下采样卷积的遥感图像目标检测算法
Remote Sensing Image Target Detection Algorithm Based on Spatial Feature Fusion Down-sampling Convolution
摘要
Abstract
In view of the large number of small targets in remote sensing images,the limited performance of existing detection algorithms,and the inherent difficulty of small-target detection,this study proposes a novel target detection algorithm based on spatial feature fusion down-sampling convolution.First,to mitigate the loss of small-target information caused by conventional down-sampling convolution modules,a spatial feature fusion down-sampling convolution module is designed to replace traditional down-sampling convolution,enabling the model to better focus on small targets during both training and inference.Second,a lightweight neck feature fusion module is introduced,incorporating a parameter-free attention mechanism to maintain detection accuracy while reducing model complexity and parameter count.Finally,WIoU is employed as a regression loss function to improve the accuracy of the regression frame.Experimental results on the DIOR remote sensing image dataset show that,compared with YOLOv8n,the proposed method achieves improvements of 2.3%in mAP50 and 2.1%in mAP50-90 while reducing the number of parameters by 0.12 M.关键词
遥感图像目标检测/YOLOv8/空间特征融合/下采样/WIoUKey words
remote sensing image object detection/YOLOv8/spatial feature fusion/downsampling/WIoU分类
信息技术与安全科学引用本文复制引用
林立恒,林珊玲,卢蓓婕,林志贤,郭太良..基于空间特征融合下采样卷积的遥感图像目标检测算法[J].红外技术,2026,48(4):476-483,8.基金项目
国家重点研发资助项目(2021YFB3600603),福建省自然科学基金资助项目(2020J01468). （2021YFB3600603）
