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基于机器视觉的作物多姿态害虫特征提取与分类方法
引用本文:李文勇,李明,陈梅香,钱建平,孙传恒,杜尚丰.基于机器视觉的作物多姿态害虫特征提取与分类方法[J].农业工程学报,2014,30(14):154-162.
作者姓名:李文勇  李明  陈梅香  钱建平  孙传恒  杜尚丰
作者单位:1. 中国农业大学信息与电气工程学院,北京 100083; 2. 国家农业信息化工程技术研究中心,北京 100097;;2. 国家农业信息化工程技术研究中心,北京 100097;;2. 国家农业信息化工程技术研究中心,北京 100097;;2. 国家农业信息化工程技术研究中心,北京 100097;;2. 国家农业信息化工程技术研究中心,北京 100097;;1. 中国农业大学信息与电气工程学院,北京 100083;
基金项目:国家自然科学基金青年基金项目(31301238);北京市自然科学基金资助项目(4132027);北京市农林科学院青年科研基金(QN201102);北京市农林科学院国际合作基金(GJHZ2013-4)
摘    要:由于野外诱捕害虫的姿态存在多样性和不确定性,使得利用机器视觉进行害虫的自动识别与计数仍然是一个难题。该文提出一种基于颜色和纹理等与形态无关的特征相结合和利用多类支持向量机分类器的多姿态害虫分类方法。通过对目标害虫图像进行不同颜色空间特征、基于统计方法的纹理特征和基于小波的纹理特征的提取,构建了6组不同组合的特征向量。将10阶交叉验证的识别率作为适应度函数值,利用遗传算法对各组特征向量进行降维筛选。最后利用基于有向无环图多类支持向量机分类器对多姿态害虫进行识别和特征组选择。结果表明,遗传算法最多可以使特征向量维数降到原来的38.89%,基于HSV三通道颜色图像的小波纹理特征组在建模时间和平均准确率方面都表现最优,可以作为一种有效的多姿态害虫分类特征选择。

关 键 词:机器视觉  图像处理  特征提取  害虫分类  多类支持向量机
收稿时间:4/7/2014 12:00:00 AM
修稿时间:7/4/2014 12:00:00 AM

Feature extraction and classification method of multi-pose pests using machine vision
Li Wenyong,Li Ming,Chen Meixiang,Qian Jianping,Sun Chuanheng and Du Shangfeng.Feature extraction and classification method of multi-pose pests using machine vision[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(14):154-162.
Authors:Li Wenyong  Li Ming  Chen Meixiang  Qian Jianping  Sun Chuanheng and Du Shangfeng
Institution:1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;;1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;
Abstract:Abstract: Pest identification and classification is time-consuming work that requires expert knowledge for integrated pest management. Automation, including machine vision combined with pattern recognition, has achieved some applications in areas such as fruit sorting, robotic harvesting, and quality detection, etc. Automatic classification and counting of pests using machine vision is still a challenge because of variable and uncertain poses of trapped pests. Therefore, using Pseudaletia separata, Conogethes punctiferalis, Helicoverpa armigera, Agrotis ypsilon with different poses as research objects, this paper presents a novel classification method for multi-pose pests based on color and texture feature groups and using a multi-class support vector machine. 320 images were taken using field samples with an original resolution of 4 288×2 848. The subimages of pests with 640×640 pixel size were obtained from original images for computational efficiency. Color features in RGB and HSV spaces, statistical texture features, and wavelet-based texture features were extracted. Six feature vector groups were constructed using those features. In order to select effective feature parameters of each group, a genetic algorithm was designed to optimize feature vectors based on 10-fold cross-validation. Finally, the one-against-one DAGMSVM (acronym as yet undefined) algorithm was applied to classify and recognize the four kinds of target pests and to find the best feature group. 80 images (60 for the training set and 20 for the testing set) were adopted for each species. Parameter numbers were calculated and analyzed after optimization, thus the best parameters were selected for each group. The training time of the SVM model and classification accuracy, which contains false negative and false positive details, were compared between pre-optimization and post-optimization. The results showed that the highest parameter optimization ratio is from the sixth feature group with a dimension reduction rate of 61.11%. Compared with the RGB and statistical texture feature group F2, the optimization ratio of HSV and statistical texture feature group F3 is much better; that is, the latter one is more suitable to pest classification. Analysis and comparison between the optimization results of feature group F5 and F6 shows that the latter one is more suitable for multi-pose pest classification. The modeling time of each group has been greatly decreased, especially the one of group F6 (about 8 s), which is the minimum time of all groups with a decreased rate of 74.5%. Average accuracies of all groups have been improved beyond 97%. The sixth group has the highest accuracy (100%). Consequently, the sixth feature group, the feature vector of the wavelet filter in HSV color space, is an effective feature set for use in the classification of multi-pose pests. In addition, we have found that the feature parameters are similar among the misclassification pest sets, which may be improved by increasing the number of sample images in the training set.
Keywords:computer vision  image processing  feature extraction  pest classification  multi-class support vector machine
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