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首页> 外文会议>IEEE International Conference on Computer Vision Workshops >Creating Roadmaps in Aerial Images with Generative Adversarial Networks and Smoothing-Based Optimization
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Creating Roadmaps in Aerial Images with Generative Adversarial Networks and Smoothing-Based Optimization

机译：利用生成的对抗网络和基于平滑的优化在航空影像中创建路线图

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Recognizing roads and intersections in aerial images is a challenging problem in computer vision with many real world applications, such as localization and navigation for unmanned aerial vehicles (UAVs). The problem is currently gaining momentum in computer vision and is still far from being solved. While recent approaches have greatly improved due to the advances in deep learning, they provide only pixel-level semantic segmentations. In this paper, we argue that roads and intersections should be recognized at the higher semantic level of road graphs - with roads being edges that connect nodes. Towards this goal we present a method consisting of two stages. During the first stage, we detect roads and intersections with a novel, dual-hop generative adversarial network (DH-GAN) that segments images at the level of pixels. At the second stage, given the pixelwise road segmentation, we find its best covering road graph by applying a smoothing-based graph optimization procedure. Our approach is able to outperform recent published methods and baselines on a large dataset with European roads.
机译：在许多实际应用中，例如无人机的定位和导航，在计算机视觉中识别空中图像中的道路和交叉路口是一个具有挑战性的问题。该问题目前在计算机视觉中获得了发展，并且距离解决之路还很遥远。尽管由于深度学习的进步，最近的方法已大大改善，但它们仅提供像素级语义分割。在本文中，我们认为应该在道路图的较高语义级别上识别道路和交叉路口-道路是连接节点的边。为了实现这一目标，我们提出了一种由两个阶段组成的方法。在第一阶段，我们使用新颖的双跳生成对抗网络（DH-GAN）检测道路和交叉路口，该网络将像素级别的图像分割。在第二阶段，给定逐像素道路分割，我们通过应用基于平滑的图优化程序来找到其最佳覆盖道路图。在具有欧洲道路的大型数据集上，我们的方法能够胜过最近发布的方法和基准。

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