基于流形学习算法的马铃薯机械损伤机器视觉检测方法  

Machine vision detecting potato mechanical damage based on manifold learning algorithm

作　　者：汪成龙[1] 李小昱[1] 武振中[1] 周竹[2] 冯耀泽[1] 

机构地区：[1]华中农业大学工学院,武汉43007 [2]浙江农林大学信息工程学院,临安311300

出　　处：《农业工程学报》2014年第1期245-252,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(61275156);湖北省自然科学基金重点项目(2011CDA033)

摘　　要：针对马铃薯表面芽眼和凹凸不平的影响,使之马铃薯机械损伤难以检测的问题,该文提出了一种基于流形学习算法的马铃薯机械损伤检测方法。首先利用马铃薯图像的显著图分割出马铃薯区域,然后利用主成分分析(principal component analysis,PCA)、等距映射(isometric mapping,Isomap)和局部线性嵌入(locally-linear embedding,LLE)3种流形学习方法提取马铃薯区域图像特征参数,然后分别建立基于3种流形特征的支持向量机(support vector machine,SVM)分类模型PCA-SVM、Isomap-SVM和LLE-SVM,利用网格搜索法(grid search)、遗传算法(genetic algorithm,GA)以及粒子群算法(particle swarm optimization,PSO)3种模型参数优化方法,优化支持向量机模型的惩罚参数c和RBF核参数g,以建立最优分类模型,最后比较3种分类模型的识别效果,确定最优分类模型。研究结果表明,PCA-SVM分类模型对训练集识别率为100%,测试集识别率为100%;Isomap-SVM分类模型对训练集识别率为100%,测试集识别率为91.7%;LLE-SVM分类模型对训练集识别率为100%,测试集识别率为91.7%,表明PCA、Isomap和LLE 3种流形学习方法用于马铃薯机械损伤检测是可行的,其中PCA-SVM分类模型检测效果最优。Buds and uneven surface of potatoes have caused problems to detect the mechanical damage based on machine vision. The lighting conditions and gray value changes of defect region have great impacts on the pixel level feature extraction. While manifold learning methods have been extensively studied in the face recognition, they have not been used for the external quality inspection of agricultural products. The manifold learning method is mainly divided into linear and nonlinear manifold learning algorithms. The nonlinear manifold learning algorithm includes isometric mapping （Isomap）, locally linear embedding （LLE）, laplacian eigenmaping （LE）. The linear algorithm is extension of the nonlinear methods such as principal component analysis （PCA） and multidimensional scaling （MDS）. In order to weaken the influence of the buds and uneven surface on potatoes mechanical damage detection, the image was characterized by using low dimensional manifolds. A mechanical damage detection method for potatoes was provided based on manifold learning. In this study, the Saliency and H images were firstly segmented on the potato regional image. The segmentation accuracies of both images are 100%. However, Saliency-H method can the potato’s location information of the image by unsupervised pattern was automatically obtained. In addition, Saliency-H method was faster （average elapsed time is 477.7ms） than H method with a high data compression rate. After the potato region images were resampled from 1024×768 to 64×64, the features of potato images were extracted from the resample images by using the three manifold learning methods： principal component analysis （PCA）, isometric mapping （Isomap） and locally linear embedding （LLE）. Thirdly, the three corresponding SVM classification models were developed based on their features. Finally the parameters of the models were optimized to develop corresponding optimal classification models by using the grid search method （grid search）, genetic algor

关 键 词：机器视觉 识别 算法 流形学习 机械损伤 马铃薯 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]