空洞卷积结合全局池化的卷积神经网络识别作物幼苗与杂草  

Recognition of crop seedling and weed recognition based on dilated convolution and global pooling in CNN

作　　者：孙俊[1] 何小飞 谭文军 武小红[1] 沈继锋[1] 陆虎[2] Sun Jun;He Xiaofei;Tan Wenjun;Wu Xiaohong;Shen Jifeng;Lu Hu(School of Electrical and Information Engineering of Jiangsu University,Zhenjiang 212013,China;School of Computer Science and Communication Engineering of Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学电气信息工程学院,镇江212013 [2]江苏大学计算机科学与通信工程学院,镇江212013

出　　处：《农业工程学报》2018年第11期159-165,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(No.31471413);江苏高校优势学科建设工程资助项目PAPD(苏政办发2011 6号);江苏省六大人才高峰资助项目(ZBZZ-019)

摘　　要：针对传统Alex Net模型参数大、特征尺度单一的问题,该文提出一种空洞卷积与全局池化相结合的多尺度特征融合卷积神经网络识别模型。通过对初始卷积层的卷积核进行膨胀,以增大其感受野而不改变参数计算量,并采用全局池化代替传统的全连接层来减少模型的参数。通过设置不同膨胀系数的初始卷积层卷积核与全局池化层类型,以及设置不同Batch Size,得到8种改进模型,用于训练识别共12种农作物幼苗与杂草,并从建立的模型中选出最优模型。改进后的最优模型与传统Alex Net模型相比,仅经过4次训练迭代,就能达到90%以上的识别准确率,平均测试识别准确率达到98.80%,分类成功指数达到96.84%,模型内存需求减少为4.20 MB。实际田间预测野芥与雀麦幼苗的准确率都能达到75%左右,说明该文最优模型对正常情况下的幼苗识别性能较好,但对复杂黑暗背景下的甜菜幼苗准确率为60%,对恶劣背景下的识别性能还有待提升。由于模型使用了更宽的网络结构,增加了特征图的多尺度融合,保持对输入空间变换的不变性,故对正常情况下不同作物幼苗与杂草的识别能力较强。该文改进模型能达到较高的平均识别准确率及分类成功率,可为后续深入探索复杂田间背景下的杂草识别以及杂草与幼苗识别装置的研制打下基础。The damage of weeds in the field to the seedlings of crops can seriously affect the photosynthesis of plant seedlings. To solve this problem, and to prevent weeds from affecting the growth of crop seedlings, it is of great significance to accurately identify crop seedlings and weeds. This paper proposed a model to identify weeds based on improved convolutional neural network（CNN）. Aiming at the problems of many parameters of traditional Alex Net model and single feature scale, this paper adjusted the network structure of the traditional model by combining dilated convolution and global pooling, and extended the single convolution kernel into multi-scale convolution kernel then fused. It can optimize the training time and achieve high precision. We regarded each convolution layer input to calculate the bulk mean and variance, and then used the batch normalization while reducing some of the characteristics of the layer figure at the same time. We also used the global pooling to take the place of the last full connection layer. The model consists of 7 convolution layers, 1 fusion layer and 4 pooling layers. In image preprocessing phase, in order to prevent the deviation of the trained model caused by the unbalanced distribution of sample numbers, we had zoomed, flipped and rotated the original pictures of dataset randomly to get the augmented dataset, and used 80% of the pictures as the train dataset and the rest as the test dataset. These pictures were quantized to 256×256 dpi for CNN（convolutional neural network） training, and the original dataset and augmented dataset were used to train models. In order to find the optimal expansion coefficient, the expanded convolution kernels with expansion factor of 2 and 4 were used respectively at the first layer of convolution kernel. In addition, we compared the global average pooling with the global maximum pooling. The results showed that the global average pooling is better, which means that the average pooling can reduce the variance of the estimated value due

关 键 词：图像识别 农作物 幼苗 杂草 空洞卷积 全局池化 多尺度特征融合 卷积神经网络 

分 类 号：S126[农业科学—农业基础科学]