In recent years, Autonomous Driving Systems (ADS) become more and more popular and reliable. Road markings are important for drivers and advanced driver assistance systems by better understanding the road environment. While the detection of road markings may suffer a lot from various illuminations, weather conditions and angles of view, most traditional road marking detection methods use fixed threshold to detect road markings, which is not robust enough to handle various situations in the real world. To deal with this problem, some deep learning-based real-time detection frameworks such as Single Shot Detector (SSD) and You Only Look Once (YOLO) are suitable for this task. However, these deep learning-based methods are data-driven even while there is no public road marking dataset. Besides, these detection frameworks usually struggle with distorted road markings and balancing between the precision and recall. We propose a two-stage YOLOv2-based network to tackle distorted road marking detection as well as to balance precision and recall. The proposed spatial transformer layer is able to handle the distorted road markings in the second stage, so as to achieve the improvement of precision. Our network is able to run at 58 FPS in a single GTX 1070 under diverse circumstances. Furthermore, we present a dataset for the public use of road marking detection tasks, which consists of 11,800 high-resolution images captured under different weather conditions. Specifically, the images are manually annotated into 13 classes with bounding boxes. We empirically demonstrate both mean average precision (mAP) and detection speed of our system over several baseline models.

近年来，自动驾驶系统（ADS）变得越来越流行和可靠。通过更好地了解道路环境，道路标记对于驾驶员和高级驾驶员辅助系统很重要。尽管道路标记的检测可能会受到各种光照，天气条件和视角的影响，但是大多数传统的道路标记检测方法都使用固定阈值来检测道路标记，这不足以应付现实世界中的各种情况。为了解决此问题，一些基于深度学习的实时检测框架（例如“单发检测器”（SSD）和“一次只看一次”（YOLO））适用于此任务。但是，即使没有公共道路标记数据集，这些基于深度学习的方法也是由数据驱动的。除了，这些检测框架通常会遇到变形的道路标记以及精度和召回率之间的平衡问题。我们提出了一个基于YOLOv2的两阶段网络，以解决变形的道路标记检测以及平衡精度和召回率的问题。所提出的空间变换器层能够在第二阶段处理变形的道路标记，从而实现精度的提高。我们的网络能够在各种情况下在一台GTX 1070上以58 FPS的速度运行。此外，我们提供了供道路标记检测任务公开使用的数据集，其中包含在不同天气条件下捕获的11,800张高分辨率图像。具体来说，将图像用边框手动注释为13类。