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Lane Detection Under Adverse Conditions Based on Dual Color Space

机译:基于双色空间的不利条件下的车道检测

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A high level of situational awareness is essential to an advanced driver assistance system. One of the most important duties of such a system is the detection of lane markings on the road and to distinguish them from the road and other objects such as shadows, traffic, etc. A robust lane detection algorithm is critical to a lane departure warning system. It must determine the relative lane position reliably and rapidly using captured images. The available literature provides some methods to solve problems associated with adverse conditions such as precipitation, glare and blurred lane markings. However, the reliability of these methods can be adversely affected by the lighting conditions. In this paper, a new method is proposed that combines two distinct color spaces to reduce interference in a pre-processing step. The method is adaptive to different lighting situations. The directional gradient is used to detect the lane marking edges. The method can detect lane markings with different complexities imposed by shadows, rain, reflection, strong sources of light such as headlights and tail lights.
机译:高水平的态势感知对于高级驾驶员辅助系统至关重要。这种系统最重要的职责之一就是检测道路上的车道标记,并将其与道路和其他物体(例如阴影,交通等)区分开来。强大的车道检测算法对于车道偏离警告系统至关重要。它必须使用捕获的图像可靠,快速地确定相对车道位置。现有文献提供了一些解决与不利条件相关的问题的方法,例如降水,眩光和模糊的车道标志。但是,这些方法的可靠性会受到光照条件的不利影响。在本文中,提出了一种新方法,该方法结合了两个不同的色彩空间以减少预处理步骤中的干扰。该方法适用于不同的照明情况。方向梯度用于检测车道标记边缘。该方法可以检测阴影,雨水,反射,强光源(例如前灯和尾灯)施加的具有不同复杂性的车道标志。

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