摘要
路面检测对于自动驾驶系统具有极其重要的作用,其具体的应用方面包括检测辅助、避障、自动导航等。基于视觉的路面检测主要就是对图像中每一个像素点进行分类,区分其是否为路面。到目前为止大部分的路面检测算法是应用于白天。在本文中,我们集中解决夜间的路面检测。我们利用一个近红外摄像头来采集夜间图像。检测时,首先利用平面反射模型来对图像中的路面部分进行拟合,然后,一个基于像素点的分类方法被用来对图像中的每一个像素点进行分类。在实验部分,我们将我们的算法与区域增长的方法进行了比较。实验证明,我们的算法相对区域增长有一定的优势。
Road detection is of high importance in different advanced driver-assistance systems. It is widely used for functionalities such as pedestrian detection, obstacle avoidance, autonomous navigation, etc. The vision-based road detection is to classify image pixels belonging to road surface or not. Up to now, most road detection algorithms are designed for working during daytime. In this paper, we mainly focus on road detection at night. A near-infrared camera which provides infrared lamps to strengthen the weak illumination is used for image capturing. Firstly a planar reflection model is proposed to fit the intensity distribution of the images pixels. Next, a pixel-based classification is applied to determine whether the pixel is on the road surface or not. In the experiments, we compare our algorithm with the region growing method. The experiments show that our approach works better in some aspects.
出处
《集成技术》
2013年第2期16-20,共5页
Journal of Integration Technology
关键词
路面检测
夜间工作
平面反射模型
监控机器人
road detection
work at night
planar reflection model
surveillance robots
