%0 Journal Article
%T 一种高分辨率遥感图像视感知目标检测算法<br>An Object Detection Algorithm with Visual Perception for High？？Resolution Remote Sensing Images
%A 李策
%A 张亚超
%A 蓝天
%A 杜少毅
%J 西安交通大学学报
%D 2018
%R 10.7652/xjtuxb201806002
%X 针对大幅面高分辨率光学遥感图像目标检测尚存在着检测精度和效率低的问题,提出了一种高分辨率遥感图像视感知目标检测算法。该算法首先通过显著区域有选择性的引导获取场景中的子区域,将计算资源转移到可能包含目标的区域中,以降低计算复杂度;然后,利用基于单次检测器(YOLO)卷积神经网络目标检测模型获取预选目标;最后,提出目标语义关联抑制对获取的预选目标进行筛选得到有效目标,能够减少虚假目标的干扰,降低虚警率。所提算法在公开NWPU_VHR？？10数据集上的平均检测精度为0？？865,高于对比算法,在包含更多高分辨率的LUT_VHRVOC？？2数据集上,比YOLO的检测效果更好。实验结果表明,所提算法提高了大幅面高分辨率遥感图像的目标检测精度。<br>An effective object detection approach with visual perception for high？？resolution remote sensing images is proposed to address the problem that the accuracy and speed of existing object detection algorithms of remote sensing images are low, especially in large？？scale and high？？resolution remote sensing images. Firstly, some sub？？regions are selected in the scene using a saliency map, and then transfer computing resources to the area that may contain objects to reduce the computational complexity. Then, pre？？selected objects are obtained by a fast learning model YOLO (you only look once). An object semantic association suppress is proposed to screen the pre？？selected objects for effective objects. It reduces the interference of false objects for reducing the false alarm probability. Experimental results on NWPU_VHR？？10 dataset show that the proposed algorithm is the best, and the mean average precision (mAP) is 86？？53％. The results of the proposed algorithm are much better than those of YOLO on LUT_VHRVOC？？2 dataset which contains more large？？scale and high？？resolution remote sensing images. It is concluded that the performance of the high？？resolution remote sensing image is improved by the proposed algorithm
%K 高分辨率遥感图像
%K 目标检测
%K 目标语义关联抑制
%K 卷积神经网络&lt
%K br&gt
%K high？？resolution remote sensing image
%K object detection
%K object semantic correlation suppression
%K convolutional neural network
%U http://zkxb.xjtu.edu.cn/oa/DArticle.aspx?type=view&id=201806002