Image recognition method, device and equipment based on hybrid neural network model

Abstract

The invention discloses an image recognition method, a device and equipment based on a hybrid neural network model, and a computer readable storage medium. The method comprises the steps of inputtinga to-be-recognized image into a convolutional auto-encoder for preprocessing; extracting image features of the preprocessed to-be-identified image by using a feature extractor constructed based on transfer learning; extracting internal time sequence features of the preprocessed to-be-identified image by using a long short-term memory network model; utilizing a feature fusion door and a feature screening door to fuse and screen the image features and the internal time sequence features to obtain target features of the recognition image; and utilizing a softmax classifier to classify the targetfeatures to obtain a classification result of the to-be-identified image. According to the method, the device, the equipment and the computer readable storage medium provided by the invention, the number of images required for training the neural network model can be greatly reduced, and the accuracy of image recognition is improved.

Description

technical field [0001] The present invention relates to the technical field of image recognition, in particular to an image recognition method, device, equipment and computer-readable storage medium based on a hybrid neural network model. Background technique [0002] In recent years, image recognition technology has developed rapidly, especially deep learning has greatly improved the accuracy of image recognition. Using deep learning to identify daily necessities can help us solve many simple and tedious manual classification problems. It can also solve the problem of difficult item management and classification. [0003] However, deep learning requires a large number of labeled samples to achieve. However, in reality, we need to obtain a large number of labeled samples, which is very labor-intensive and material resources. Therefore, it is difficult to train a neural network model with high recognition accuracy simply by using the traditional neural network model. [0...

AI Technical Summary

Problems solved by technology

[0005] The object of the present invention is to provide an image recognition method, device, device and computer-readable storage medium based on a hybrid neural network model, to solve the problem of high recognition accuracy but complicated training of deep learning networks in the prior art, and traditional neural network models The problem of simple training but low image recognition accuracy

Images