基于SE-Hardnet网络的无人机图像目标匹配算法  

UAV image target matching algorithm based on SE-Hardnet network

作　　者：苏文博 房群忠 徐保树 张程硕 SU Wenbo;FANG Qunzhong;XU Baoshu;ZHANG Chengshuo(School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110870,Liaoning,China)

机构地区：[1]沈阳工业大学信息科学与工程学院,辽宁沈阳110870

出　　处：《沈阳工业大学学报》2024年第5期693-701,共9页Journal of Shenyang University of Technology

基　　金：辽宁省“兴辽英才”项目(XLYC2002100)。

摘　　要：针对无人机对目标进行匹配定位过程中,面临图像旋转变化及视角尺寸过小导致的图像特征提取困难等问题,提出了一种融合候选区域检测与SE-Hardnet特征提取网络的无人机目标图像匹配算法。通过Edge Boxes算法检测候选区域,结合SE-Hardnet网络进行特征提取,实现了目标图像的精确匹配。实验结果表明,所提算法在图像发生角度、尺寸变化时,具有更高的匹配正确率和鲁棒性,在近距离条件下图片数据集中的匹配正确率比现阶段图像匹配算法高8%~11%。为无人机目标定位提供了一种可行和有效的手段。In order to address the problems in target matching and localization by unmanned aerial vehicles(UAVs),such as the difficulties of feature extraction caused by image rotation variations between UAV images and target reference images and small image sizes from the UAV perspective,a UAV target image matching algorithm combining the candidate region detection and the SE-Hardnet feature extraction network was proposed.The Edge Boxes algorithm was used to detect candidate regions,and the SE-Hardnet network was employed for regional feature extraction,for precise target image matching by comparing feature similarities.Experimental results demonstrate that the proposed algorithm exhibits higher matching accuracy and robustness under the condition of image angle and size variations.In close-range scenarios,the matching accuracy within the image dataset surpasses that of current image matching algorithms by 8%to 11%,providing a feasible and effective solution for UAV target positioning.

关 键 词：图像匹配 候选区域检测 Edge Boxes算法 特征提取 注意力机制 SE-Hardnet网络 相似性度量 无人机目标定位 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]