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Lane Marking Detection by Extracting White Regions with Predefined Width from Bird's-Eye Road Images

机译:通过从鸟瞰道路图像中提取具有预定义宽度的白色区域来进行车道标记检测

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Detecting lane markings on roads from in-vehicle camera images is very important because it is one of the fundamental tasks for autonomous running technology and safety driving support system. There are several lane markings detection methods using the width information, but most of these are considered to be insufficient for oblique markings. So, the primary intent of this paper is to propose a detecting lane markings method robust to orientation of markings. In this work, we focus on the width of lane markings standardized by road act in Japan, and propose a method for detecting white lane markings by extracting white regions with constant predefined width from bird's-eye road images after segmentation such as categorical color area one. The proposed method is based on the constrained Delaunay triangulation. The proposed method has a merit that can be measure an exact width for oblique markings on the bird's-eye images because it can be obtained perpendicular width for edge. The effectiveness of the proposed method was shown by experimental results for 187 actual road images taken from an in-vehicle camera.
机译:从车载摄像机图像检测道路上的车道标记非常重要,因为这是自动驾驶技术和安全驾驶支持系统的基本任务之一。有几种使用宽度信息的车道标记检测方法,但是大多数方法被认为不足以用于倾斜标记。因此,本文的主要目的是提出一种对标记方向具有鲁棒性的检测道标记方法。在这项工作中,我们着眼于日本通过道路行为标准化的车道标记的宽度,并提出了一种通过分割后从鸟瞰道路图像中提取具有恒定预定义宽度的白色区域(例如分类颜色区域)来检测白色车道标记的方法。 。所提出的方法是基于约束的Delaunay三角剖分。所提出的方法的优点是可以测量鸟瞰图像上倾斜标记的精确宽度,因为可以获得边缘的垂直宽度。从车载摄像机拍摄的187个实际道路图像的实验结果证明了该方法的有效性。

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