基于改进残差网络的园林害虫图像识别  

Pest Image Recognition of Garden Based on Improved Residual Network

作　　者：陈娟[1] 陈良勇 王生生[1] 赵慧颖 温长吉[2] CHEN Juan;CHEN Liangyong;WANG Shengsheng;ZHAO Huiying;WEN Changji(College of Computer Science and Technology,Jilin University,Changchun 130012,China;College of Information and Technology,Jilin Agricultural University,Changchun 130118,China)

机构地区：[1]吉林大学计算机科学与技术学院,长春130012 [2]吉林农业大学信息技术学院,长春130118

出　　处：《农业机械学报》2019年第5期187-195,共9页Transactions of the Chinese Society for Agricultural Machinery

基　　金：吉林省科技发展计划项目(20180101334JC;20190302117GX;20160520099JH);吉林省发展改革委创新能力建设(高技术产业部分)项目(2019C053-3)

摘　　要：针对北方园林害虫识别问题,提出了一种基于改进残差网络的害虫图像识别方法。首先,采用富边缘检测算法,将中值滤波、Sobel算子和Canny算子相结合,对害虫图像进行边缘检测;然后,改进残差网络中的残差块,通过添加卷积层和增加通道数提取更多的害虫图像特征,并将贝叶斯方法运用于改进后的网络中,优化超参数;最后,将预处理的害虫图像输入神经网络中,利用分块共轭算法优化网络权重。对38种北方园林害虫进行了识别,试验结果表明,在相同数据集下,与3种传统害虫识别方法相比,本文方法的平均识别准确率平均提高9. 6个百分点,加权平均分数分别提高16. 3、10. 8、4. 5个百分点。Plant pest and disease is one of the three major natural disasters. Pest identification tends to consume a lot of labor, and it is difficult for naked eyes to quickly and accurately identify pest species. However, there still exist some drawbacks in the traditional deep learning algorithms for pest recognition, such as gradient explosion or gradient disappearance in deep neural networks, degradation and overfitting caused by limited sample size. In order to address these problems and improve the accuracy of pest recognition, a pest image recognition method based on improved residual network was proposed. Firstly, the pest images in the data set were converted to grayscale before edge detection was performed on them by using Rich-edge. To obtain a fine-lined pest image, the Rich-edge was combined with median filtering, Sobel operator and Canny operator to detect the edges of the pest images. Among them, the median filter effectively eliminated the salt and pepper noise, the Sobel operator accurately detected the position information, and the Canny operator detected the weak edge. The images after edge detection were quantized to be 224 pixel×224 pixel for training and classification. Then the obtained pest image set was used to train the deep neural network, which was a variant of standard residual network with additional convolution layers and channels for extracting more image features. And the dropout layer was added to each residual block of the network to prevent overfitting when it was trained on a relatively small data set. Besides, the regularization hyper parameters of the network were designed to be optimized by Bayesian method which adaptively adjusted the size of the hyper parameters with the adjustment of weights during network training. The weights of the proposed network were optimized through the Block-cg algorithm. In the optimization algorithm, the block diagonal was used to approximate the curvature matrix, which improved the convergence of the Hessian matrix;and independent conjugate gradient

关 键 词：图像识别 害虫控制 残差网络 贝叶斯方法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S126[自动化与计算机技术—计算机科学与技术]