再生稻收割机的视觉导航路径检测方法
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摘要
采用机器视觉技术研究再生稻收割机导航路径检测方法.根据农田再生稻图像特点,基于HSV空间的S分量结合Otsu算法得到初始分割阈值T;为更好地保留不同成熟度再生稻植株特征,加入修正因子-a,得到分割阈值T-a二值化图像.将土壤路径从再生稻植株中分割出来,根据形成的植株左右边界区域特征,提出逐行扫描图像动态检测导航路径的中间离散点集,利用基于已知点的Hough变换检测出稻桩行间导航路径.结果表明:处理一幅像素419×310的图像平均耗时0.064 s,具有较好的实时性,对稻叶交叠现象具有较强的适应性,拟合的导航线符合人眼视觉感官判断.
A method of identifying the vision navigation path for ratooning rice harvester is proposed based on machine vision technology to reduce the rate of rice straw rolled. Based on the characteristics of ratooning rice image in the farmland,an initial segmentation threshold T was obtained based on S variate in HSV space,by Otsu algorithm. In order to maintain the integral feature of ratooning rice area under different maturity stages,a modified factor-a was incorporated to get segmentation threshold T-a,which was used to binarize the grayscale image. Then the ratooning rice area was divided into sections of left and right by harvester pathway. A dynamic method for identifying discrete points in the navigation path was proposed based on the boundary feature and scanned image.Lastly,the vision navigation path was identified by the known point Hough transform in the ratooning rice area. The results demonstrated that the average time of processing a 419×310 pixel image was 0.064 s,which met the timing requirement in the field. Moreover,navigation line accords with human visual recognition,and exerts strong adaptability to noises like overlapped leaf.
A method of identifying the vision navigation path for ratooning rice harvester is proposed based on machine vision technology to reduce the rate of rice straw rolled. Based on the characteristics of ratooning rice image in the farmland,an initial segmentation threshold T was obtained based on S variate in HSV space,by Otsu algorithm. In order to maintain the integral feature of ratooning rice area under different maturity stages,a modified factor-a was incorporated to get segmentation threshold T-a,which was used to binarize the grayscale image. Then the ratooning rice area was divided into sections of left and right by harvester pathway. A dynamic method for identifying discrete points in the navigation path was proposed based on the boundary feature and scanned image.Lastly,the vision navigation path was identified by the known point Hough transform in the ratooning rice area. The results demonstrated that the average time of processing a 419×310 pixel image was 0.064 s,which met the timing requirement in the field. Moreover,navigation line accords with human visual recognition,and exerts strong adaptability to noises like overlapped leaf.
引文
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[2]迟德霞,任文涛,由佳翰,等.水稻插秧机视觉导航信息获取试验[J].沈阳农业大学学报,2015(5):575-580.
[3]TORU T.Research in autonomous agriculture vehicles in Japan[J].Computer and Electronics in Agriculture,2000,25(1):133-153.
[4]唐泽华.基于机器视觉的步行插秧机自主导航关键技术研究[D].杭州:浙江理工大学,2013.
[5]吴佳艺.基于机器视觉的农林环境导航路径生成算法研究[D].杭州:浙江工业大学,2009.
[6]朱鸿,陈鸿飞.福建省再生稻研究现状与展望[J].亚热带农业研究,2006,2(3):170-175.
[7]OTSU N.A threshold selection method from gray-lever histogram[J].IEEE Trans on SMC,1979,29(1):62-66.
[8]张振升,朱名日.基于HSI颜色空间的蔗糖结晶图像分割方法[J].计算机工程与应用,2011(11):190-193.
[9]冈萨雷斯.数字图像处理[M].2版.阮秋奇,译.北京:电子工业出版社,2003.
[10]赵德升.基于机器视觉的精确喷施智能除草装置杂草实时识别技术[D].镇江:江苏大学,2009.
[11]曹倩,王库,杨永辉,史小磊.基于TMS320DM642的农业机器人视觉导航路径检测[J].农业机械学报,2009(7):171-175.
[12]刘兆祥,陈艳,籍颖,等.基于机器视觉的农业车辆路径跟踪[J].农业机械学报,2009(S1):18-22.
[13]张甜.高茬水田耕整路径机器视觉识别方法研究[D].武汉:华中农业大学,2014.
[14]黄小刚.水田除草机器人视觉导航路径与参数获取方法研究[D].广州:华南理工大学,2013.