一种基于卷积神经网络的烟叶等级识别方法

目的:解决烟叶分级准确率不高的问题.方法:提出一种改进的基于卷积神经网络的烟叶分级模型,根据VGG16网络结构,以 自定义的方式搭建网络模型;将空洞卷积代替原有的传统卷积,增加图像感受野的同时避免了图像特征的损失,并将激活函数改为Leaky_relu,修正数据的分布,解决ReLU函数的硬饱和问题;用41种等级的烟叶图片...

A method of tobacco leaf grade recognition based on convolutional neural network

Objective:To solve the problem of low accuracy of tobacco grading. Methods:An improved tobacco leaf grading model based on convolutional neural network was proposed. According to the VGG16 network structure, the network model was built in a custom way. The traditional convolution was replaced by the hole convolution, which increased the image receptive field while avoiding. The loss of image features was changed, and the activation function was changed to Leaky_relu. The data distribution was corrected, and the hard saturation problem of the ReLU function was solved. 41 levels of tobacco leaf pictures were used for testing. Results:The grading accuracy rate of the test algorithm was 95.89%, which was 10.46% higher than the traditional SVM algorithm, and 7.87% higher than the classic VGG16 algorithm. The loss rate finally converged to 0.13. Conclusion:Compared with the original model and traditional feature extraction methods, this algorithm has improved the accuracy of tobacco leaf classification.