Research on Web GUI Image Recognition System Based on Improved Convolutional Neural Network

In this paper, a MobileNet V2 convolutional neural network depending on L2 regularization method, a amended particle swarm model and Dropout method are constructed in a bid for enhancing the accuracy and speed of Web GUI image recognition for establishing Web Web GUI image recognition system of Web GUI test. Firstly, an improved MobileNet V2 convolutional neural network is constructed. Secondly, the basic models of L2 regularization method, improved particle swarm method and Dropout method are studied, the improved MobileNet V2 convolution neural network optimization algorithm is proposed, and the basic model of image processing is designed. Finally, simulation analysis is carried out on Web GUI image recognition through using the amended convolutional neural network on basis of MINIST data set as the research object. The simulation results illustrate that the amended convolutional neural network proposed in this study is more accurate and efficient in Web GUI image recognition.