计算机工程与应用2024，Vol.60Issue(1)：207-216,10.DOI:10.3778/j.issn.1002-8331.2303-0375
注意力特征融合的快速遥感图像目标检测算法
Fast Remote Sensing Image Object Detection Algorithm Based on Attention Feature Fusion
摘要
Abstract
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.关键词
遥感图像/目标检测/YOLO/注意力机制/特征融合Key words
remote sensing image/object detection/YOLO/attention mechanism/feature pyramid分类
计算机与自动化引用本文复制引用
吴建成,郭荣佐,成嘉伟,张浩..注意力特征融合的快速遥感图像目标检测算法[J].计算机工程与应用,2024,60(1):207-216,10.基金项目
国家自然科学基金(11905153,61701331). （11905153,61701331）
