基于计算机视觉的芒果检测与分级研究

Research on Mango Detection and Classification by Computer Vision

针对目前芒果的外观品质分级主要采取人工方法所存在的不足,提出了一种基于计算机视觉和极限学习机神经网络(ELM)模型的芒果分级方法。首先,利用图像处理方法对拍摄的芒果图像进行预处理;然后,根据芒果的外观特征提取芒果面积、等效椭圆长短轴之比、H分量均值和缺陷面积所占百分比4个特征参数,作为模型的输入向量,并以芒果的3个等级级别为模型输出向量。在模型的建立过程中,采用粒子群优化算法(PSO)对ELM随机给定的输入权值矩阵和隐层阈值进行寻优,最后以实验获得的数据对模型进行训练和测试。结果表明:使用粒子群算法优化后的极限学习机模型(PSOELM)与单纯的ELM、传统的BP和SVM相比,具有更高的分级精度,为水果的等级分级提供了一种新的方法。

For the shortage of artificial rank method in mango appearance rank classification ,a method of mango appearance rank classification was presented based on computer vision technology and extreme learning machine （ ELM ） Neural Net-work.Through the computer vision technology gaining mango image ,and the image processing method was used to carry on pre-processing to the mango image ,then according to the appearance charicterristic to mango appearance rank classifi-cation,four charicterristic parameters such as mango area , the ratio of the equivalent ellipse axle , H weight average and percentage of defect area were extracted and selected as the the input vector of the model , the rank of mango as output vector .In the process of the establishment of the model , the input weight matrix and hidden layer threshold of ELM were optimized by particle swarm optimization （PSO）, finally,using the experimental data collected to train the model and then predict the output ,the results show that compared with ELM and BP and SVM Neural Network , the PSOELM has higher prediction precision , a new method was provided for the rank classification of fruit .