Research on Recognition and Classification Technology Based on Deep Convolutional Neural Network

Abstract

Deep convolutional neural network is one of the most popular research topics in the field of computer vision. It has the function of extracting image feature information, has strong nonlinear classification ability, fast learning speed, and can be used for image recognition and classification. This paper makes use of its image recognition and classification function to carry on the research of its recognition and classification technology in oil painting schools. Through the ResNet network structure of a deep convolutional neural network, a data set is constructed by load data function, and then embedded into a SEBlock model, the accuracy and generalization ability of image recognition and classification of the deep convolutional neural network can be greatly improved. Among them, the SE model has strong effectiveness and generalization ability. For example, the accuracy of the SE-ResNet-34 is 1.73% higher than that of the ResNet-34, and the accuracy of the SE-ResNet-50 has reached that of the ResNet-101. The SE model is applied to the deep convolutional neural network to improve classification accuracy and reduce errors.