基于空间特征融合下采样卷积的遥感图像目标检测算法OA

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.