Abstract: Accurate recognition of seed potato bud eyes is an important prerequisite for realizing intelligent cutting. In order to solve the problems of misjudgment and difficulty in obtaining 3d position information of seed potato bud eye directly due to the influence of light in machine vision recognition, a new method of seed potato bud eye recognition based on laser 3D reconstruction was proposed. First, the ROI area in the process of the point cloud was determined to eliminate the influence of the background in the acquisition process by industrial camera to match the line laser continuous acquisition mobile chips of laser light image, using the triangulation principle to obtain the depth of the information found on the surface of a potato, light gray centroid method was utilized to extract the center, to get point cloud data found on the surface of a potato. Then, according to the point cloud sparsity, random noise and skirt noise in the point cloud were removed and obtained to improve the quality of the high point cloud and reduce the bud misjudgment rate. On the premise of retaining the features of the eyes, the voxel filtering algorithm was used to sparse the point clouds to improve the efficiency of the eyes recognition. Finally, the point cloud normal vector was obtained by plane fitting to the local neighborhood of arbitrary point on the seed potato surface, and the weighted covariance matrix was built to parameterize the seed potato surface point cloud. According to the dynamic threshold set by the matrix eigenvalue size, the surface point clouds of seed potato were initially screened, and the candidate points for seed potato sprout eye discrimination were obtained. European clustering algorithm was used to obtain the point cloud clusters of candidate points, and the largest eigenvalue point in each point cloud cluster was selected as the key point. The Angle cosine value between the center line vector and normal vector composed of the key points and other points in the neighborhood was used to screen the key points again and finally determine the location of each eye of the seed potato. The experimental results showed that the recognition rate of bud eye was 95.13% and the recognition rate of bud eye error was 4.87%, which could provide reference for bud eye recognition in intelligent cutting of potato seed potatoes.

Key words: seed potato, laser point cloud, three-dimensional reconstruction, feature extraction, bud eye recognition

摘要： 种薯芽眼的准确识别是实现智能切块的重要前提。为解决种薯芽眼机器视觉识别易出现误判和不易获取芽眼三维位置信息而导致切块不均匀的问题,提出一种基于激光三维重建的种薯芽眼识别方法。确定点云获取过程中ROI区域消除采集过程中背景的影响,通过工业相机与线激光器相配合连续采集移动种薯的激光光条图像获取其点云数据;根据点云密度去除随机噪声和裙边噪声,提高点云质量,降低芽眼误判率。采用体素滤波算法稀疏点云,提高识别效率;通过对种薯表面任意点的局部邻域进行平面拟合后获取点云法向量,构建加权协方差矩阵参数化种薯表面点云,根据矩阵特征值大小设定的动态阈值对种薯表面点云进行初步筛选,得到种薯芽眼判别的候选点,采用欧式聚类算法获取候选点的点云簇,选取每个点云簇中最大特征值点为关键点,利用计算关键点和邻域内其他点构成的中心线连线向量与法向量夹角余弦值对关键点再次筛选,最终确定种薯各个芽眼位置。试验结果表明,芽眼识别率为95.13%,芽眼误识别率为4.87%,可为马铃薯种薯智能化切块时芽眼识别提供参考。

关键词: 种薯, 激光点云, 三维重建, 特征提取, 芽眼识别