论文标题
托托尔：一种拓扑感知道路细分的对抗性学习方法
TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation
论文作者
论文摘要
大多数最先进的方法从空中图像中提取道路的方法都依赖于经过训练的CNN将Road像素标记为前景，其余图像作为背景。 CNN通常通过最大程度地减少像素损失来训练，这并不是生产可保留道路网络全球连通性的二进制口罩的理想选择。为了解决这个问题，我们引入了为我们的目的量身定制的对抗性学习（AL）策略。一个天真的人会将分割网络视为发电机，并将其输出以及地面真相分割提供给歧视器。然后，它将共同训练发电机和鉴别器。我们将证明这还不够，因为它并不能捕获大多数错误是本地的事实，并且需要这样对待。取而代之的是，我们使用一个更复杂的歧视器，该歧视器返回标签金字塔，描述道路网络的哪些部分在几个不同的尺度上正确。这个歧视者及其返回的结构化标签使我们的方法具有优势，我们将表明它在具有挑战性的路标数据集中优于最先进的标签。
Most state-of-the-art approaches to road extraction from aerial images rely on a CNN trained to label road pixels as foreground and remainder of the image as background. The CNN is usually trained by minimizing pixel-wise losses, which is less than ideal to produce binary masks that preserve the road network's global connectivity. To address this issue, we introduce an Adversarial Learning (AL) strategy tailored for our purposes. A naive one would treat the segmentation network as a generator and would feed its output along with ground-truth segmentations to a discriminator. It would then train the generator and discriminator jointly. We will show that this is not enough because it does not capture the fact that most errors are local and need to be treated as such. Instead, we use a more sophisticated discriminator that returns a label pyramid describing what portions of the road network are correct at several different scales. This discriminator and the structured labels it returns are what gives our approach its edge and we will show that it outperforms state-of-the-art ones on the challenging RoadTracer dataset.
