基于改进灰度共生矩阵和粒子群算法的稻飞虱分类
针对稻飞虱远程实时识别采集图像质量不高而无法使用颜色特征的问题,应用灰度共生矩阵提取的纹理特征值对稻飞虱分类进行了研究。采用自行设计的拍摄装置采集稻飞虱图像,经过一系列预处理后得到去掉背景的稻飞虱灰度图像;对灰度图像采用改进的灰度共生矩阵提取纹理特征值,再用反向传播BP（back propagation）神经网络和参数改进粒子群算法优化BP神经网络分别进行训练和测试,以此检验纹理特征值的识别效果和粒子群算法的优化效果。试验用Matlab验证算法,对白背飞虱、灰飞虱和褐飞虱共300个样本进行了训练和测试,结果表明基于参数选择改进粒子群算法优化BP神经网络的识别率总体达到了95%,比直接用BP神经...
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| Published in | 农业工程学报 Vol. 30; no. 10; pp. 138 - 144 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
江苏省现代设施农业技术与装备工程实验室，南京 210031
2014
南京农业大学工学院，南京 210031 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.3969/j.issn.1002-6819.2014.10.017 |
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| Abstract | 针对稻飞虱远程实时识别采集图像质量不高而无法使用颜色特征的问题,应用灰度共生矩阵提取的纹理特征值对稻飞虱分类进行了研究。采用自行设计的拍摄装置采集稻飞虱图像,经过一系列预处理后得到去掉背景的稻飞虱灰度图像;对灰度图像采用改进的灰度共生矩阵提取纹理特征值,再用反向传播BP（back propagation）神经网络和参数改进粒子群算法优化BP神经网络分别进行训练和测试,以此检验纹理特征值的识别效果和粒子群算法的优化效果。试验用Matlab验证算法,对白背飞虱、灰飞虱和褐飞虱共300个样本进行了训练和测试,结果表明基于参数选择改进粒子群算法优化BP神经网络的识别率总体达到了95%,比直接用BP神经网络的识别率高,而且经过Matlab测试,训练时间只用了0.5683s,说明粒子群算法更满足实时性要求。 |
|---|---|
| AbstractList | 针对稻飞虱远程实时识别采集图像质量不高而无法使用颜色特征的问题,应用灰度共生矩阵提取的纹理特征值对稻飞虱分类进行了研究。采用自行设计的拍摄装置采集稻飞虱图像,经过一系列预处理后得到去掉背景的稻飞虱灰度图像;对灰度图像采用改进的灰度共生矩阵提取纹理特征值,再用反向传播BP（back propagation）神经网络和参数改进粒子群算法优化BP神经网络分别进行训练和测试,以此检验纹理特征值的识别效果和粒子群算法的优化效果。试验用Matlab验证算法,对白背飞虱、灰飞虱和褐飞虱共300个样本进行了训练和测试,结果表明基于参数选择改进粒子群算法优化BP神经网络的识别率总体达到了95%,比直接用BP神经网络的识别率高,而且经过Matlab测试,训练时间只用了0.5683s,说明粒子群算法更满足实时性要求。 TP391.41; 针对稻飞虱远程实时识别采集图像质量不高而无法使用颜色特征的问题，应用灰度共生矩阵提取的纹理特征值对稻飞虱分类进行了研究。采用自行设计的拍摄装置采集稻飞虱图像，经过一系列预处理后得到去掉背景的稻飞虱灰度图像；对灰度图像采用改进的灰度共生矩阵提取纹理特征值，再用反向传播BP（back propagation）神经网络和参数改进粒子群算法优化 BP 神经网络分别进行训练和测试，以此检验纹理特征值的识别效果和粒子群算法的优化效果。试验用Matlab验证算法，对白背飞虱、灰飞虱和褐飞虱共300个样本进行了训练和测试，结果表明基于参数选择改进粒子群算法优化BP神经网络的识别率总体达到了95%，比直接用BP神经网络的识别率高，而且经过Matlab测试，训练时间只用了0.5683s，说明粒子群算法更满足实时性要求。 |
| Abstract_FL | The rice planthopper images acquired by remote real-time recognition system usually have poor quality, and hence it is impossible to classify rice planthoppers using the color features of rice planthopper images. This study proposed to extract texture features of images based on gray level co-occurrence matrix (GLCM) and used the texture features to classify rice planthoppers. A H-shape mobile photographing device designed by us was used to obtain color images of rice planthoppers. The color images were grayed by formula, and then the background of images was removed using Otsu image segmentation method to generate binary images followed by calculation through the binary image coordinates. The GLCM was improved to extract texture features of images without background. Specifically, the center of gravity was determined by coordinates of the images and considered as the center to construct GLCM. The images of the rice planthopper were copied into the sub images with 160 pixels×160 pixels based on the center. Using multiple annular routes, the features of rice planthopper gray images were extracted including energy, entropy, moment of inertia and correlation. In the training and testing experiment of the extracted features, back propagation (BP) nerve network and optimized BP nerve network based on parametric selection -improved particle swarm optimization algorithm were individually used to train and classify the rice planthopper, and the training time and identification rate of each method were compared. A total of 300Sogatella,Laodelphax andNilaparvata lugens with 100 samples for each type of rice planthopper was trained. The training time using the optimized BP nerve network based on improved particle swarm optimization algorithm was only 0.5683 seconds, which was far less than that (29.5772 seconds) using BP neural network. Based on the BP neural network, the identification rate reached 80% forSogatella, 90% forLaodelphax, and 95% forNilaparvata lugens. Based on the improved particle swarm optimization algorithm-optimized BP nerve network, the identification rate reached 90% forSogatella, 95% forLaodelphax, and 100% forNilaparvata lugens. Therefore, the identification rate of the optimized BP neural network based on parametric selection-improved particle swarm optimization algorithm was higher than that of BP neural network. Furthermore, the shorter training time using the optimized BP neural network based on parametric selection-improved particle swarm optimization algorithm than using the BP neural network suggested that the former could better meet the requirement of real time optimization. |
| Author | 邹修国 丁为民 陈彩蓉 刘德营 |
| AuthorAffiliation | 南京农业大学工学院,南京210031 江苏省现代设施农业技术与装备工程实验室,南京210031 |
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| Author_FL | Zou Xiuguo Liu Deying Ding Weimin Chen Cairong |
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| Keywords | 稻飞虱 灰度共生矩阵 neural networks 粒子群 神经网络 particle swarm optimization rice planthopper gray level co-occurrence matrix classification 图像识别 image recognition 分类 |
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| Notes | 11-2047/S neural networks;image recognition;classification;particle swarm optimization;rice planthopper;gray level co-occurrence matrix The rice planthopper images acquired by remote real-time recognition system usually have poor quality, and hence it is impossible to classify rice planthoppers using the color features of rice planthopper images. This study proposed to extract texture features of images based on gray level co-occurrence matrix（GLCM） and used the texture features to classify rice planthoppers. A H-shape mobile photographing device designed by us was used to obtain color images of rice planthoppers. The color images were grayed by formula, and then the background of images was removed using Otsu image segmentation method to generate binary images followed by calculation through the binary image coordinates. The GLCM was improved to extract texture features of images without background. Specifically, the center of gravity was determined by coordinates of the images and considered as the center to |
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| Publisher | 江苏省现代设施农业技术与装备工程实验室，南京 210031 南京农业大学工学院，南京 210031 |
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| Snippet | 针对稻飞虱远程实时识别采集图像质量不高而无法使用颜色特征的问题,应用灰度共生矩阵提取的纹理特征值对稻飞虱分类进行了研究。采用自行设计的拍摄装置采集稻飞虱图像,经过... TP391.41; 针对稻飞虱远程实时识别采集图像质量不高而无法使用颜色特征的问题，应用灰度共生矩阵提取的纹理特征值对稻飞虱分类进行了研究。采用自行设计的拍摄装置采集稻飞... |
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| SubjectTerms | 分类 图像识别 灰度共生矩阵 神经网络 稻飞虱 粒子群 |
| Title | 基于改进灰度共生矩阵和粒子群算法的稻飞虱分类 |
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