Research on Classification and Recognition of Object Image Based on Convolutional Neural Network

Abstract

Image recognition and classification using a convolution neural network is an important application of image processing. How too reasonably to design the convolutional layer of the convolutional neural network, the number of hidden layers and optimize the parameters of the convolutional neural network to ensure high accuracy and efficiency in image recognition and classification are an extremely important part. The core of this thesis is the model design and parameter optimization of deep convolutional neural networks. This paper mainly designs, implements, optimizes and adjusts the model structure of the convolutional network of Tensor Flow framework platform. We redesigned the convolutional neural network model to a depth of 19 layers and used two data sets for training, testing and parameter optimization. Experimental results show that the convolution neural network models presented in this paper is superior to other neural network models in accuracy and efficiency of image recognition and classification, and has a good guiding role in solving practical engineering problems.