野外环境下图像中坑区域的提取方法

在野外环境中有很多坑区域,这对野外工作机器人的移动带来了困难,因此引入视觉方法来实现对坑的检测。首先根据工程要求去除掉一部分不满足大小的疑似区域,再利用坑区域边缘梯度去除掉一部分疑似区域,之后计算与椭圆相似度来确定灰度分割阈值,并通过分析坑与椭圆相似度曲线来确定相似度阈值,以从疑似区域中分离出坑区域。经过使用200幅不同角度、场景和坑数量的图像进行测试,结果表明该方法能够在复杂野外环境下很好地提取出坑区域,对坑轮廓的规整度不敏感,能够适应复杂的环境,是一种有效的方法。

Image segmentation method of pit area in wild environment

It is difficult for robot to move in wild environment because of pit areas, so a visual coping method was put forward to detect those pit areas. Firstly, according to project requirements, a part of suspected areas with small size were removed, as well as some the suspected areas with edge gradient. Secondly, the oval similarity was calculated to determine gray level segmentation threshold, and the similarity threshold was confirmed by analyzing the oval similarity curve, which was used to separate pit areas from the suspected pit areas. At last, the simulation results on 200 pictures with different angles, scenes and pit umbers show that the proposed method can be applied to extract pit area in complex environment, and is also not sensitive to outline regularity of pit area; besides, it can adapt to complex environment.