农业工程学报
農業工程學報
농업공정학보
2014年
1期
245-252
,共8页
汪成龙%李小昱%武振中%周竹%冯耀泽
汪成龍%李小昱%武振中%週竹%馮耀澤
왕성룡%리소욱%무진중%주죽%풍요택
机器视觉%识别%算法%流形学习%机械损伤%马铃薯
機器視覺%識彆%算法%流形學習%機械損傷%馬鈴藷
궤기시각%식별%산법%류형학습%궤계손상%마령서
computer vison%identification%algorithms%manifold learning%mechanical damage%potatoes
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。
針對馬鈴藷錶麵芽眼和凹凸不平的影響，使之馬鈴藷機械損傷難以檢測的問題，該文提齣瞭一種基于流形學習算法的馬鈴藷機械損傷檢測方法。首先利用馬鈴藷圖像的顯著圖分割齣馬鈴藷區域，然後利用主成分分析（principal component analysis，PCA）、等距映射（isometric mapping，Isomap）和跼部線性嵌入（locally-linear embedding，LLE）3種流形學習方法提取馬鈴藷區域圖像特徵參數，然後分彆建立基于3種流形特徵的支持嚮量機（support vector machine，SVM）分類模型PCA-SVM、Isomap-SVM和LLE-SVM，利用網格搜索法（grid search）、遺傳算法（genetic algorithm，GA）以及粒子群算法（particle swarm optimization，PSO）3種模型參數優化方法，優化支持嚮量機模型的懲罰參數c和RBF覈參數g，以建立最優分類模型，最後比較3種分類模型的識彆效果，確定最優分類模型。研究結果錶明，PCA-SVM 分類模型對訓練集識彆率為100%，測試集識彆率為100%；Isomap-SVM 分類模型對訓練集識彆率為100%，測試集識彆率為91.7%；LLE-SVM 分類模型對訓練集識彆率為100%，測試集識彆率為91.7%，錶明 PCA、Isomap和LLE 3種流形學習方法用于馬鈴藷機械損傷檢測是可行的，其中PCA-SVM分類模型檢測效果最優。
침대마령서표면아안화요철불평적영향，사지마령서궤계손상난이검측적문제，해문제출료일충기우류형학습산법적마령서궤계손상검측방법。수선이용마령서도상적현저도분할출마령서구역，연후이용주성분분석（principal component analysis，PCA）、등거영사（isometric mapping，Isomap）화국부선성감입（locally-linear embedding，LLE）3충류형학습방법제취마령서구역도상특정삼수，연후분별건립기우3충류형특정적지지향량궤（support vector machine，SVM）분류모형PCA-SVM、Isomap-SVM화LLE-SVM，이용망격수색법（grid search）、유전산법（genetic algorithm，GA）이급입자군산법（particle swarm optimization，PSO）3충모형삼수우화방법，우화지지향량궤모형적징벌삼수c화RBF핵삼수g，이건립최우분류모형，최후비교3충분류모형적식별효과，학정최우분류모형。연구결과표명，PCA-SVM 분류모형대훈련집식별솔위100%，측시집식별솔위100%；Isomap-SVM 분류모형대훈련집식별솔위100%，측시집식별솔위91.7%；LLE-SVM 분류모형대훈련집식별솔위100%，측시집식별솔위91.7%，표명 PCA、Isomap화LLE 3충류형학습방법용우마령서궤계손상검측시가행적，기중PCA-SVM분류모형검측효과최우。
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 algorithm (GA) and particle swarm optimization (PSO). The best three classification models were obtained through comparing the recognition results of SVM classification models. Test results showed that the training set recognition rate of PCA-SVM classification model was 100%, the test set recognition rate was 100%. The best parameter optimization method was grid search, the best number of features was 40, the test parameter c was equal to 27.8576 g. The training set recognition rate of Isomap-SVM classification model was 100%, the test set recognition rate was 91.7%, the best parameter optimization method was GA, the best number of features is 4, the test parameter c was equal to 27.8576 g. The training set recognition rate of LLE-SVM classification model was 100%, the test set recognition rate was 91.7%, the best parameter optimization method was PSO, the best number of features is 19, the test parameter c equals 0.1000, g equals 18.8827. These results indicate that potatoes mechanical damage detection is feasible using three manifold learning methods including PCA, Isomap and LLE. PCA-SVM classification model is the best classification model.
