Globe country image identification method based on convolutional neural network

Abstract

The invention discloses a globe country image identification method based on the convolutional neural network. The method comprises steps that firstly, a globe country image data set is constructed through collecting various types of images of various countries on commonly-used teaching globes by means of data acquisition and data enhancement, and multiple images of the different countries from different spatial locations and angles under different lighting conditions and different focusing conditions are collected; secondly, each image in the data set is compressed and preprocessed; and thirdly, a new convolutional neural network model is designed based on characteristics of classic convolutional neural network models MobileNet and DenseNet, the new model is trained based on the collecteddata set, the model is made to learn the image characteristics of each country on the globe, and classification is further carried out. The method is advantaged in that the characteristics of the models MobileNet and DenseNet are integrated in the designed identification model, and relatively high identification accuracy and relatively low model complexity are realized.

Description

technical field [0001] The invention relates to the fields of deep learning and image recognition, in particular to a convolutional neural network-based image recognition method for globe countries. Background technique [0002] At present, most of the research on the country recognition of the globe adopts the traditional image recognition method. First, it needs to manually extract features, such as using sift, hog, lbp, haar and other image feature operators to extract image features, and then use such as svm, adaboost and other classifiers. Classification. In this method, the process of manually extracting features is cumbersome, and it is difficult to extract more advanced features, so the recognition effect is not ideal. [0003] The convolutional neural network does not require tedious manual feature extraction of images, but directly inputs the original image into the network, uses convolution operations to extract low-level features at lower layers, and then combin...

AI Technical Summary

Problems solved by technology

In this method, the process of manually extracting features is cumbersome, and it is difficult to extract more advanced features, so the recognition effect is not ideal

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