Abstract

In order to improve the cross-domain applicability of road segmentation, a feature transfer based adversarial domain adaptation method is presented for cross-domain road extraction. The presented method consists of two main parts, a feature transfer network and an adversarial domain adaption network. The feature transfer network transforms the images of the source domain into the feature space of the target domain. The adversarial domain adaption network learns to distinguish whether the results come from source domain or target domain, to promote the backbone network of road extraction to learn the common features of the road. The experimental results showed that the proposed method improved the generalization ability of the road extraction network and could extract the road target from cross-domain images accurately and effectively. The proposed method can realize the road extraction across domain without any annotation of target domain, so it has good application value.

摘要

为了提高道路分割的跨域适用性，提出了一种基于特征转移的对抗域自适应方法进行跨域道路提取。所提出的方法由两个主要部分组成，一个特征传输网络和一个对抗域适应网络。特征传递网络将源域的图像转换为目标域的特征空间。对抗域自适应网络学习区分结果来自源域还是目标域，促进道路提取的骨干网络学习道路的共同特征。实验结果表明，该方法提高了道路提取网络的泛化能力，能够准确有效地从跨域图像中提取道路目标。