自然光照下基于粒子群算法的农业机械导航路径识别  

Guidance Line Recognition of Agricultural Machinery Based on Particle Swarm Optimization under Natural Illumination

作　　者：孟庆宽[1,2] 张漫[3] 杨耿煌[1,2] 仇瑞承 项明[3] 

机构地区：[1]天津职业技术师范大学自动化与电气工程学院,天津300222 [2]天津市信息传感与智能控制重点实验室,天津300222 [3]中国农业大学现代精细农业系统集成研究教育部重点实验室,北京100083

出　　处：《农业机械学报》2016年第6期11-20,共10页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(31571570);国家国际科技合作专项(2015DFG12280)

摘　　要：针对农业机械视觉导航线提取易受光照变化影响及常规导航线识别算法实时性低、抗干扰能力差等问题,对自然光照条件下基于机器视觉的农业机械导航路径识别技术进行了研究。首先,在YCr Cb颜色模型的基础上构建与光照无关的Cg分量,选择2Cg-Cr-Cb特征因子对图像进行灰度化处理,以降低光照变化对图像分割的影响;然后,采用改进K-means聚类方法进行图像分割,将绿色作物信息从土壤背景中分离出来,并通过形态学滤波方法滤除二值图像中存在的杂草干扰信息;最后,根据图像中作物行的特点建立作物行直线方程约束模型,利用粒子群算法对作物行直线进行寻优求解,进而得到导航线。实验结果表明,不同光照条件下对2Cg-Cr-Cb灰度图像进行图像分割,可以清晰完整地将作物从土壤背景中分离出来,分割图像受光照变化影响较小并且不会引入背景噪声;基于粒子群算法的导航线检测方法可以快速准确地提取出导航路径,对于不同农田作物和作物不同生长阶段具有较高的适应性,相比于常规导航线识别算法具有实时性高、鲁棒性好等优点。In farmland with complex environment,guidance line recognition of agricultural machinery based on machine vision is subjected to illumination variation,weed noise,etc. In addition,the conventional path detection algorithms have the drawbacks of low processing speed and poor antiinterference. The visual navigation path detection under natural environment was conducted. Firstly,to reduce the influence of illumination changes on the quality of image segmentation,Cg component was constructed on the base of YCr Cb color mode and the 2Cg-Cr-Cb factor was selected to preprocess the image. Secondly,the clustering segmentation of the image was performed based on improved K-means algorithm to achieve the respective clusters of soil and green crop information. Then, the weed interference information in the binary image was eliminated by morphological filtering algorithm so as to obtain the complete and clear crop information. Finally,according to the characteristics of the crop rows in the image,linear equation constraints of crop rows were established. An algorithm of crop lines detection based on particle swarm optimization（ PSO） was designed. Experiment results showed that theimage segmentation based on 2Cg-Cr-Cb gray image can effectively identify crop from soil background under different illumination conditions. The segmentation images were less affected by change of illumination and no background noise was contained. The guidance line recognition method based on PSO can quickly and accurately detect the navigation line. Furthermore,it had good fitness for different crops and nice adaptability for different crop growth stages in the farmland. Compared with conventional guidance line recognition algorithms,the designed algorithm had the advantages of high speed and good robustness.

关 键 词：农业机械 机器视觉 导航路径识别 颜色模型 粒子群算法 

分 类 号：S24[农业科学—农业电气化与自动化]