基于改进全卷积网络的棉田冠层图像分割方法  

Segmentation method for cotton canopy image based on improved fully convolutional network model

作　　者：刘立波 程晓龙 赖军臣 Liu Libo;Cheng Xiaolong;Lai Junchen(College of Information Engineering,Ningxia University,Yinchuan 750021,China;Wujiaqu Municipal Bureau of Agriculture,Wujiaqu 831300,China)

机构地区：[1]宁夏大学信息工程学院,银川750021 [2]五家渠市农业局,五家渠831300

出　　处：《农业工程学报》2018年第12期193-201,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(61751215);西部一流大学科研创新项目(ZKZD2017005);宁夏大学研究生创新项目(GIP2017048)

摘　　要：针对传统的全卷积网络分割精度低、效果差等问题,该文提出一种结合条件随机场的改进全卷积网络棉田冠层图像分割方法。首先通过提取和学习图像特征对全卷积网络进行训练以优化其分割性能,得到初步分割结果和训练后的全卷积网络模型;接着将初步分割结果以像素和像素对应的分类向量形式输入到条件随机场中,同时结合像素间相对关系构建能量函数再进行训练,对初步分割结果进行优化得到训练后的条件随机场模型;进而通过验证过程对全卷积网络和条件随机场模型参数进一步调优,得到最优的全卷积网络和条件随机场;最后结合最优的全卷积网络和条件随机场实现棉田冠层图像分割并进行试验。试验结果表明:该方法的平均像素准确率为83.24%,平均交并比为71.02%,平均速度达到0.33 s/幅,与传统的全卷积网络分割性能相比分别提升了16.22和12.1个百分点,改进效果明显;与Zoom-out和CRFas RNN(conditional random fields as recurrent neural networks)分割方法进行对比,平均像素准确率分别提升了4.56和1.69个百分点,平均交并比分别提升了7.23和0.83个百分点;与逻辑回归方法和SVM(support vector machine)方法进行对比,平均像素准确率分别提升了3.29和4.01个百分点,平均交并比分别提升了2.69和3.55个百分点。该文方法在背景复杂、光照条件复杂等环境下可以准确分割出冠层目标区域,鲁棒性较好,可为棉花生长状态自动化监测提供参考。Using computer vision technology to monitor cotton growing state is the development trend of cotton production informatization, and is also the widely used automation technology. Cotton field canopy image segmentation under natural scenes is an important part for monitoring growth status of cotton. Since the existing methods are not good in the image segmentation of cotton fields in the environment of light and background, and the adaptive ability is not strong, an image segmentation method based on the fully convolution network and conditional random field was proposed. Firstly, fully convolutional network was used to extract the image features, and the fully convolutional network was trained by back propagation algorithm and stochastic gradient descent（SGD）to obtain the initial segmentation results; This network model is an improvement on the traditional network model convolutional neural network（CNN）, which was widely used in image classification. The full connect layer is transformed into a convolution layer, so that the fully convolutional network＇s output image as input image size, so it can be applied in the field of image segmentation. For achieve the FCN segmentation model structure, VGG16 was chosen as a basic network structure. And for improve the accuracy of segmentation results, we design the skip structure which can combine pool3 pool4 pool5 feature pictures to combine the FCN-8 s. Secondly, the initial segmentation results were input into the conditional random field, used the classification vector of image pixels and the relative relationship between the pixels to construct the energy function and train the conditional random field, obtained the best performance conditional random field; conditional random field is an undirected graph model, this model builds a graph on the relationship between image pixels and image pixels. Each pixel has different label, conditional random field improve the quality of the segmentation results and optimal segmentation results by minimizing the graph ene

关 键 词：农作物 图像分割 算法 棉花冠层 全卷积网络 条件随机场 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]