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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:高水平的情境意识对于先进的驾驶辅助系统至关重要。这种系统的最重要职责之一是检测道路上的车道标记,并将它们与道路和其他物体区分开,如阴影,流量等。强大的车道检测算法对车道脱离警告系统至关重要。它必须可靠地确定相对通道位置,并使用捕获的图像快速地确定相对通道位置。可用文献提供了一些方法来解决与不利条件相关的问题,如降水,眩光和模糊的车道标记。然而,这些方法的可靠性可能受到照明条件的不利影响。在本文中,提出了一种新方法,其结合了两个不同的颜色空间来减少预处理步骤中的干扰。该方法适用于不同的照明情况。定向梯度用于检测车道标记边缘。该方法可以检测具有由阴影,雨,反射,强光源施加不同复杂的车道标记,如前灯和尾灯。

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