注意力特征融合的快速遥感图像目标检测算法OACSTPCD

Aiming at the challenges of complex backgrounds,numerous small targets,and difficulty in feature extraction in remote sensing images,a fast remote sensing image object detection algorithm based on attention feature fusion—YOLO-Aff is proposed.This algorithm designs a backbone network module(ECALAN)with channel attention and a blur pool(BP)module to reduce the loss caused by downsampling.In addition,a feature pyramid network(SPD-FPN)with no stride convolution is used to combine the SimAM attention feature fusion module(CBSA)to enhance the cross-scale feature fusion performance of the features.Finally,Wise-IoU is used as the coordinate loss of the network to optimize the sample imbalance problem.The experimental results show that YOLO-Aff achieves an mAP value of 96%on the NWPU VHR-10 dataset,which is 2.9 percentage points higher than the original algorithm,and provides a new solution for fast and high-precision object detection of remote sensing images.