| 葡萄套袋机器人目标识别方法 |
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| 引用本文： | 杨庆华,刘灿,荀一,鲍官军,王志恒,黄鹏程.葡萄套袋机器人目标识别方法[J].农业机械学报,2013,44(8):234-239. |
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| 作者姓名： | 杨庆华 刘灿 荀一 鲍官军 王志恒 黄鹏程 |
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| 作者单位： | 浙江工业大学;浙江工业大学;浙江工业大学;浙江工业大学;浙江工业大学;浙江工业大学 |
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| 基金项目： | 国家自然科学基金资助项目(51075363)、浙江省自然科学基金杰出青年团队资助项目(R1090674)和浙江省特种装备制造和先进加工技术重点实验室开放基金资助项目(2011EM002) |
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| 摘 要： | 针对水平棚架栽培模式下采集的单幅葡萄果树图像，提出了结合葡萄颜色与形状特征的目标识别定位方法，获得果穗的中心线和长度特征参数。通过提取葡萄图像的|G-R|+|G-B|色差图，利用Sobel算子进行边缘提取。构建葡萄果粒轮廓的数学模型进行Hough变换，实现葡萄果粒的初步识别。结合葡萄果穗的颜色、纹理特征以及果粒分布较为集中的特点判断Hough变换检测出的圆区域是否为果粒。综合利用识别出的果粒信息找到葡萄图像的外接矩形完成目标提取。对78幅图像进行测试，正确识别出葡萄区域的图像为70幅，正确识别率约为90%。
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| 关 键 词： | 葡萄 套袋机器人 机器视觉 Hough变换 识别 |
Target Recognition for Grape Bagging Robot |
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| Yang Qinghu,Liu Can,Xun Yi,Bao Guanjun,Wang Zhiheng and Huang Pengcheng.Target Recognition for Grape Bagging Robot[J].Transactions of the Chinese Society of Agricultural Machinery,2013,44(8):234-239. |
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| Authors: | Yang Qinghu Liu Can Xun Yi Bao Guanjun Wang Zhiheng and Huang Pengcheng |
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| Institution: | Zhejiang University of Technology;Zhejiang University of Technology;Zhejiang University of Technology;Zhejiang University of Technology;Zhejiang University of Technology;Zhejiang University of Technology |
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| Abstract: | Aimed at the image of the simple grapevine taken in the pergola trellis, a grape recognition algorithm combined with color and texture feature of color image was suggested in order to get the center line and the length of the grape. Firstly, Sobel operation was used to get the edge of the |G-R|+|G-B| chromatic aberration image. Then, berries of the grape were detected by Hough transform according to the mathematical model of the outline of the grape berries. The circles detected by the Hough transform were judged by the feature of the color and texture of grape berries and the concentrated berries. At last, the shape parameters of the grape bunch were determined by the information of the berries after judged, which was needed by bagging automation of grape. The experimental results showed that the method of grape recognition was effective in segmenting the comparison of grapes. There were totally 78 images used in test, and 70 of them were correctly recognized. The correct recognition of grapes reached to 90%. |
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