| 基于分区域特征点聚类的秧苗行中心线提取 |
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| 引用本文： | 廖娟,汪鹞,尹俊楠,张顺,刘路,朱德泉.基于分区域特征点聚类的秧苗行中心线提取[J].农业机械学报,2019,50(11):34-41. |
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| 作者姓名： | 廖娟 汪鹞 尹俊楠 张顺 刘路 朱德泉 |
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| 作者单位： | 安徽农业大学,安徽农业大学,安徽农业大学,安徽农业大学,安徽农业大学,安徽农业大学 |
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| 基金项目： | 国家重点研发计划项目(2018YFD0700304)和安徽省自然科学基金项目(1708085QF148) |
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| 摘 要： | 为了准确检测水稻秧苗行中心线，提出了基于分区域特征点聚类的秧苗行中心线提取方法。采用2G-R-B特征因子和Otsu法分割秧苗和背景；通过分区域统计秧苗像素点分布提取秧苗行的候选特征点，利用特征点间近邻关系对特征点进行聚类，确定秧苗行数和各秧苗行的起始点；基于秧苗成行栽植特点引入“趋势线”，利用点到该直线的距离与距离阈值作比较，筛选出远离各行趋势线的点，并将其去除；对筛选后的每一行特征点用最小二乘法进行直线拟合，获取秧苗行中心线。实验结果表明，该算法具有较强的抗噪性能，提取秧苗行中心线的准确率达95.6%，与标准Hough变换和随机Hough变换算法相比，处理一幅分辨率为320像素×237像素的彩色图像平均耗时短，能够实现水田秧苗行中心线的准确提取，可为插秧机自主行走提供可靠的导航信息。
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| 关 键 词： | 水稻插秧机 视觉导航 秧苗行中心线 分区域 特征点聚类 |
| 收稿时间： | 2019/6/10 0:00:00 |
Detection of Seedling Row Centerlines Based on Sub-regional Feature Points Clustering |
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| LIAO Juan,WANG Yao,YIN Junnan,ZHANG Shun,LIU Lu and ZHU Dequan.Detection of Seedling Row Centerlines Based on Sub-regional Feature Points Clustering[J].Transactions of the Chinese Society of Agricultural Machinery,2019,50(11):34-41. |
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| Authors: | LIAO Juan WANG Yao YIN Junnan ZHANG Shun LIU Lu and ZHU Dequan |
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| Institution: | Anhui Agricultural University,Anhui Agricultural University,Anhui Agricultural University,Anhui Agricultural University,Anhui Agricultural University and Anhui Agricultural University |
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| Abstract: | In order to extract rice seedling rows accurately, a detection method of centerlines of rice seedling row based on sub-regional feature points clustering was proposed. 2G-R-B characteristic factor and Otsu method were used to separate seedling and background from RGB rice seedling image. By sub-regional analyzing the distribution of seedling pixels, candidate feature points of seedling row were extracted. Then feature points were clustered with the nearest neighbor relationship between feature points, and the number of seedling rows and starting points of each seedling row were determined. According to the characteristics of row planting of seedlings, trend line was introduced to refine feature points. The real feature points indicating seedling rows were obtained by comparing the shortest distance of candidate point with its corresponding trend line with a distance threshold value. Afterwards, the centerlines were detected by fitting a straight line with the least square method. The experimental results showed that the proposed method achieved good anti-noise performance. The accuracy of centerlines detection was 95.6%, but the traditional Hough method and the randomized Hough method can only reach 84.1% and 89.9%, respectively. The average processing time of a 320 pixels×237 pixels color image was less than that of the two other algorithms. It can be seen that the proposed algorithm had the advantages of high real time and high accuracy, which can accurately extract seedling row centerlines, and the research result provided navigation parameters for an automatic rice transplanter walking along the seedling row in paddy fields. |
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| Keywords: | rice transplanter visual navigation seedling row centerline sub-region feature points clustering |
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