基于SOM-K-means算法的番茄果实识别与定位方法

Recognition and Localization Method of Tomato Based on SOM-K-means Algorithm

为解决多个番茄重叠黏连时难以识别与定位的问题,提出一种基于RGBD图像和K-means优化的自组织映射(Self-organizing map,SOM)神经网络相结合的番茄果实识别与定位方法。首先,利用RGBD相机拍摄番茄图像,对图像进行预处理,获取果实的轮廓信息;其次,提取果实轮廓点的平面和深度信息,筛选后进行处理;再次,将处理后的数据输入到采用K-means算法优化的SOM神经网络中,得到点云聚类结果;最后,根据聚类点,通过坐标转换得到世界坐标信息,拟合得到各个番茄的位置和轮廓形状。以果实识别的正确率和定位结果的均方根误差(RMSE)为指标对该算法进行验证和分析,采集80幅图像共366个番茄样本,正确识别率为87.2%,定位结果均方根误差(RMSE)为1.66 mm。与在二维图像上利用Hough变换进行果实识别的试验进行对比分析,进一步验证了本文方法具有较高的准确性和较强的鲁棒性。

A method of tomatoes segmentation based on RGBD depth images and K-means optimized SOM neural network was proposed,aiming to solve the problem of automatic recognizing and localizing difficulties caused by fruits overlapping and adherence.Firstly,the contours information of the fruits was obtained from preprocessed images taken by an RGBD camera.Secondly,two-dimensional information and depth information of the points of contours were filtered and processed.Thirdly,the processed information was used as the input to the SOM neural network optimized by the K-means algorithm for training and a model for the point cloud clustering was established.Finally,the position and contour shape of each tomato were obtained.To verify the performance of the algorithm,the correct rate and the root mean square error of the fruit recognition results was used as evaluation indicators.Totally 80 pictures containing 366 tomatoes were taken as the sample,and accuracy,precision,sensitivity and specificity were taken as evaluation indicators.The correct rate was 87.2%,the root mean square error was 1.66 mm.It was proved that the method had higher accuracy and better robustness compared with the method for two-dimensional images based on Hough transform.This method solved the problem of occlusion of tomato fruits in real environment to a certain extent,and provided a new idea for combining the three-dimensional coordinate information and self-organizing neural network for fruit segmentation.