The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lane-wise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery which can capture a large area in a short period of time by introducing an aerial lane marking dataset. In this work, we propose a Symmetric Fully Convolutional Neural Network enhanced by Wavelet Transform in order to automatically carry out lane marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of number of lane marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a very high accuracy in pixel-wise localization of lane markings without using 3rd-party information. In this work, we introduce the first high-quality dataset used within our experiments which contains a broad range of situations and classes of lane markings representative of current transportation systems. This dataset will be publicly available and hence, it can be used as the benchmark dataset for future algorithms within this domain.

本文介绍了一种基于对称全卷积神经网络和小波变换的航空车道标线自动分割方法，该方法在使用新型数据增强步骤和自定义损失函数的前提下，在不使用第三方信息的情况下实现了车道标线的高精度像素定位。同时，本文介绍了我们在实验中使用的第一个高质量数据集，该数据集可公开使用，并且可以用作此领域未来算法的基准数据集。

Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery
  using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural
  Networks

Aerial LaneNet：利用小波增强代价敏感的对称全卷积神经网络进行航拍道路标线语义分割