基于最小二乘法的温室番茄垄间视觉导航路径检测  

Navigation Line Detection of Tomato Ridges in Greenhouse Based on Least Square Method

作　　者：王新忠[1] 韩旭[1] 毛罕平[1] 刘飞[1] 

机构地区：[1]江苏大学现代农业装备与技术省部共建教育部重点实验室,镇江212013

出　　处：《农业机械学报》2012年第6期161-166,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金资助项目(60575020);江苏省农机三项工程资助项目(NJ2011-46);江苏省科技支撑计划资助项目(BE2011346);江苏高校优势学科建设工程资助项目(苏财教[2011]8号);苏州市科技支撑计划资助项目(SN201137);昆山市农业科技计划资助项目(KN1110)

摘　　要：针对温室非结构作业环境和复杂背景下作业机器人路径识别检测问题开展研究。在HSI颜色空间分析番茄垄间道路图像在各分量的分布特性,提出了基于机器视觉的垄间加热管敏感区域提取方法,依据I分量直方图采用最大类间方差法进行图像自适应阈值分割,对分割后二值图像利用目标区域的边缘提取算法获得导航离散点簇。根据最小二乘法原理对导航离散点簇拟合得到2条加热管边缘线,在此基础上给出中心导航基准线检测算法,并针对光照不均和作物遮挡对导航路径检测进行了实验。实验表明,与Hough变换算法相比,该算法简单快速,对光照不均具有良好的鲁棒性,能够准确提取目标敏感区域的边缘信息,对不同遮盖率番茄垄间导航路径提取正确率达91.67%。The problem of detect navigation line by machine vision under non-structural environment and complex background in greenhouse was addressed. After analyzing the distribution characteristics of the road images in tomato ridges based on HSI color space, a method was proposed for extracting the sensitive region of the heat pipe. Firstly, the binary images were generated from the road images by using Otsu segmentation method based on /-histogram. The discrete set of navigation points was obtained by using edge extraction algorithm in accordance with the binary images. And then, the two sidelines of the heat pipe were obtained by using the least square method to fit the discrete points, on the basis of this, the navigation line detection algorithm was proposed. Finally, the influences of the illumination in homogeneity and the different covering on navigation line detection were investigated. The experimental results indicated that in comparison with Hough transform, the algorithm was simple and fast, as well as robust with illumination in homogeneity, the edge information of target region could be extracted accurately and the recognition correct rate under different covering was 91.67%.

关 键 词：视觉导航 温室 番茄 路径检测 HSI颜色空间 

分 类 号：S126[农业科学—农业基础科学]