基于最大熵和边缘信息的非结构化道路检测

针对非结构化道路因场景干扰因素多而难以准确检测的问题,文中提出了一种基于最大熵和边缘信息相结合的非结构化道路检测算法。根据熵值大的地方,图像灰度相对较均匀;熵值小的地方,图像灰度离散性较大的特性,采用二维最大熵做道路初分割。同时,结合道路边缘信息解决道路检测因光照不均、阴影和水迹等因素干扰而导致道路检测不准确的问题。实验结果表明,该算法能够准确检测出光照、阴影、水迹的路面,并且不受道路类型的影响,满足实时性要求。

Unstructured Road Detection Based on Edge Information and Maximum Entropy Segmentation

Aiming at the problem that the unstructured roads are difficult to accurately detect due to the various scene interference factors, an unstructured road detection algorithm based on the combination of maximum entropy and edge information was proposed. In this paper, the two-dimensional maximum entropy was used for the initial segmentation of the road because of the fact that the gray level of the high entropy fields of image was relatively uniform and vice versa. Furthermore, the road edge information was applied to further improve the inaccurate problem of road detection caused by uneven illumination, shadow and water stain. The experimental results indicated that the algorithm could accurately detect the road surface with light, shadow and water without the effect of the road environment and meet requirements of real-time.