Research On Image Recognition Algorithm Based On Convolutional Neural Network

With the rapid development of information technology,the Internet,especially the mobile Internet among them,has increasingly become a vital part of the people's lives.People are increasingly keen to use pictures to chat and comment on various websites and mobile applications.In the face of these fast-growing pictures,how to better identify these pictures,and more quickly and effectively explore the hidden behind these pictures.Information is becoming more and more important.Therefore,improving the recognition rate of images by convolutional neural networks is to solve these problems.Image recognition is an important research direction in the field of image research,and it is also a hot research topic in machine vision,which has very significant significance.Deep learning,in recent years,has achieved many results in image,voice,text and so on.At the same time,deep learning occupies a dominant position in the field of artificial intelligence,and is widely used and concerned in daily life.The traditional image recognition method requires artificial design features,and is relatively dependent on image recognition experienced researchers,and the traditional method has low image recognition rate.With the development of the Internet and information technology,the large amount of image data generated in the context of big data,the traditional identification method has not been able to meet our needs.Deep learning is a multi-layered network structure that automatically simulates and extracts features by simulating the human brain,giving full play to the advantages of big data.Therefore,this paper combines deep learning and image recognition to study how to improve the recognition rate of images,which has certain research space and research value.Firstly,this paper discusses the method of image recognition and the principle of convolutional neural network.At the same time,it also studies the model of convolutional neural network learning and its method theory,and analyzes the feature extraction method and recognition method of image.Secondly,through the analysis of convolutional neural network algorithm and image recognition method,in order to improve the recognition rate of images,an improved convolutional neural network model is proposed.The main content is to improve the activation function in the convolutional neural network.The improved function absorbs the smoothness of the Sigmoid function and the sparseness and fast convergence of the ReLU function,which is helpful for improving the network generalization.Experiments show that the advantages of the improved model are confirmed,and the recognition rate of the image has been improved.Finally,in order to solve the problem of over-fitting of convolutional neural networks,a convolutional neural network with Dropout technology is proposed.The main idea of the algorithm is that some of the activation units are suppressed when the data is trained,but these activation units may be activated at the next training.This property weakens the joint adaptability between the neuron nodes and avoids the over-fitting problem.