基于Hough变换和Fisher准则的垄线识别算法

为了提高农业机械自主作业视觉导航的精度,基于田间作物垄行的特点,首先选择作物的绿色为特征提取垄行结构;然后针对Hough变换原理提取垄线存在的问题,根据垄线点空间关系,运用Fisher准则函数进行反压缩处理,并将Fisher准则函数值作为垄线样本点疏密程度和方向势大小的度量,优化了Hough变换识别多垄线的条件,得出了多垄识别统一模型。试验结果表明,作物垄线定位的准确性、适应性均得到提高,而且能够避免较大面积杂草等影响,从而克服了传统Hough变换提取多垄线的不足,对农田机器视觉导航应用具有一定参考价值。

Crop Rows Detection Based on Hough Transform and Fisher Discriminant Criterion Function

In this paper green components are used to separate the crop rows from its soil background images.To determine the detection peaks and verify lines in Hough transform,a powerful tool for lines extraction from images in noisy or degraded environment,the conventional Fisher discriminant criterion function is modified to project the sample points in an accumulator into a variable.This is regarded as an efficient measurement for the density and orientation of the points distributing collinearly.An optimal mathematical model for identifying multi-rows is presented.Experimental results show that the algorithm can efficiently eliminate the effect of the weeds,and its accuracy and robustness are improved compared with the conventional Hough transform.And it is useful for the row-recognition system.