The Improvement of Image Recognition Accuracy in Visual Communication by Convolutional Neural Network Algorithm

As the demand for image recognition technology in the field of visual communication increases, existing methods still have the problem of insufficient accuracy when dealing with complex scenes and diverse image content. This paper aims to study the application of convolutional neural network (CNN) algorithm in visual communication to improve the accuracy of image recognition. This paper first constructs a large dataset containing a variety of visual communication images to ensure the diversity and sufficiency of the data. Then, data preprocessing, including image normalization, denoising, and data enhancement, is performed to improve the robustness of the model. Then, a CNN model is designed, a multi-layer convolutional layer and pooling layer structure is built, image features are extracted, and then a fully connected layer is added for classification. Finally, an appropriate loss function and optimization algorithm are selected, the model is trained on the training set, and the performance is evaluated on the validation set. After adopting the CNN algorithm, the accuracy of image recognition is increased to 99.5%, which verifies the effectiveness and advantages of CNN in visual communication. It provides important technical support for the further development of the field of visual communication.