基于Xception与迁移学习的中药饮片图像识别研究OACSTPCD

In order to achieve accurate and fast recognition of images of 60 kinds of the traditional Chinese medicine pieces,a dataset of 13 088 images of common traditional Chinese medicine pieces is built,and the transfer learning method is used to train and recognize the images of the traditional Chinese medicine pieces on the basis of the Xception convolutional neural network model in the depth learning algorithm.The initial learning rate for the model training is set as 0.01,the Nesterov momentum hyperparameter in the optimizer is set as 0.9,and the training times are set as 100.The results are obtained as follows.The accuracy of the training set is 100%,the accuracy of the verification set is 97.42%,and the accuracy of the test set is 97.26%.The recognition ability of the model is evaluated and analyzed by combining the confusion matrix.In comparison with the traditional machine learning algorithms that rely on extracting image features of the traditional Chinese medicine pieces,the proposed model has better classification effect and stronger generalization ability.