CAJ | 학술논문

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。
침대도비슬원정실시식별채집도상질량불고이무법사용안색특정적문제，응용회도공생구진제취적문리특정치대도비슬분류진행료연구。채용자행설계적박섭장치채집도비슬도상，경과일계렬예처리후득도거도배경적도비슬회도도상；대회도도상채용개진적회도공생구진제취문리특정치，재용반향전파BP（back propagation）신경망락화삼수개진입자군산법우화 BP 신경망락분별진행훈련화측시，이차검험문리특정치적식별효과화입자군산법적우화효과。시험용Matlab험증산법，대백배비슬、회비슬화갈비슬공300개양본진행료훈련화측시，결과표명기우삼수선택개진입자군산법우화BP신경망락적식별솔총체체도료95%，비직접용BP신경망락적식별솔고，이차경과Matlab측시，훈련시간지용료0.5683s，설명입자군산법경만족실시성요구。
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.