基于超像素显著性的无人机引导区域提取

为解决无人机视觉定位与导航中引导区域的提取问题, 提出了一种基于超像素显著性的引导区域提取方 法。 该方法首先利用 SLIC(Simple Linear Iterative Clustering)算法将地面图像划分为内部相似度较高的超像素区 域, 通过计算超像素的显著性值得到超像素显著性图, 再基于先验规则从超像素显著性图中提取合适的准引导 区域, 最后计算各区域的匹配概率, 从而得到高显著性和高匹配率的引导区域。 实验结果表明, 该引导区域提 取方法在测试集上的准确率和召回率分别为 89%与 87%, 基本满足无人机视觉定位与导航的要求。

Navigation Region Extraction Based on Saliency of Superpixels

In order to solve the problem of extracting navigation region in vision positioning and navigation of unmanned aerial vehicle, a navigation region extraction algorithm based on superpixels'saliency is proposed. Firstly,the land image is divided into superpixel regions with high internal similarity by using SLIC(Simple Linear Iterative Clustering),the saliency value of each superpixel is calculated to obtain the superpixel saliency map.And the quasi guidance areas are extracted based on the navigation region filtering rules.Finally, navigation areas are obtained through estimating the matching probability of the quasi guidance areas.The results of our experiment show that the precision and recall of the proposed method are respectively 89%and 87%on the test set,which means the method can basically meet the requirements of UAV(Unmanned Aerial Vehicle) vision positioning and navigation.