针对FPGA等片上资源较为缺乏的处理器，提出一种应用于辅助驾驶的快速车道线偏移警告方法. 方法
面向单目视觉识别应用；首先采用基于区域统计方式的二值化处理进行图像增强，并利用515车道线模板匹配方
法代替传统的边沿检测去除原图像中的大量干扰信息，最后采用压缩型霍夫变换对匹配结果进行车道线提取. 与
传统霍夫变换相比，大大降低了运算量，减小了内存使用量. 实验证明，该算法对车道线的识别非常有效，满足实
时性需求，并已成功移植到FPGA上.
This paper proposes a robust approach for fast lane departure warning to assist safe driving. With
a monocular vision technique, binarization is realized using a region counting algorithm to enhance the image.
To find lane boundaries from the binary image, use a 515 lane template instead of the traditional edge
detection method. This method can remove much noise in the original image, improve recognition accuracy
and reduce computation load. To extract lane lines from the matched result, compressive Hough transform is
used, which reduces the required memory space as compared with traditional techniques. Experimental results
show that the proposed algorithm optimizes lane recognition and works robustly in real-time. It can also be
implemented with field programmable gate array (FPGA).
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