基于深度学习的板栗分级方法研究

Research on the method of Chinese chestnut grading based on deep learning

板栗品质分级对板栗产品的标准化和均一性有重要影响,精确的分类有助于板栗商品标准化,发挥出各等级板栗的最大价值。针对中国板栗分级分类大多依靠机器与人工,存在效率低、准确度低等问题,提出利用深度学习方法实现板栗分级分类的自动化与智能化。对经典卷积神经网络LeNet-5模型进行了改进,增加5层卷积层和2层池化层以加深网络,从而更准确地提取板栗特征,同时输入层修改为图像大小256×256的彩色图像;激活函数改进为Leaky ReLu,并加入Dropout算法缓解过拟合现象;使用Adam作为优化器对网络参数进行优化。将改进后的LeNet-5模型与初始LeNet-5模型、AlexNet和VGG16模型进行对比,发现改进后的LeNet-5模型在测试集上识别平均精确率为99.68%、准确率为99.34%、召回率为99.35%,优于其他3种模型,且识别1个样本用时仅0.19 s,改进后的LeNet-5模型可以实现对板栗良好的分级分类,满足工厂对板栗自动分级的需要。

The quality grading of Chinese chestnut had an important influence on the standardization and homogeneity of Chinese chestnut products.Accurate classification was helpful to the standardization of Chinese chestnut products and gave full play to the max⁃imum value of each grade of Chinese chestnut.In view of the low efficiency and low accuracy of Chinese chestnut classification,which mostly depended on machines and manpower,this paper proposed to use the deep learning method to realize the automation and intelli⁃gence of Chinese chestnut classification.The classical convolutional neural network LeNet-5 model was improved by adding 5 layers of convolution layer and 2 layers of pooling layer to deepen the network,so as to extract chestnut features more accurately.At the same time,the input layer was modified to the image size of 256×256 color images.The activation function was improved to Leaky ReLu,and the Dropout algorithm was added to alleviate the over fitting phenomenon.Adam was used as the optimizer to optimize the network parameters.Comparing the improved LeNet-5 model with the original LeNet-5 model,AlexNet and VGG16 model,it was found that the improved LeNet-5 model had an average recognition accuracy of 99.68%,an accuracy of 99.34%,and a recall of 99.35%on the test set,which was superior to the other three models.It took only 0.19 seconds to recognize a sample.The improved LeNet-5 model could achieve a good classification of Chinese chestnuts and meet the needs of factories for automatic classification of Chinese chestnuts.