Exploration and Research of Convolutional Neural Networks in Image Recognition

In recent years, convolutional neural networks (CNNs) have achieved significant progress in the field of image recognition. However, their generalization capability and model complexity still require optimization in practical applications. This paper aims to further explore and optimize the performance of CNNs in image recognition tasks. First, the paper reviews the applications and techniques of CNNs in areas such as agricultural image recognition, animal recognition, smart city construction, and facial recognition. Second, the basic structure and training process of CNNs are introduced. A corresponding CNN model is constructed and trained using the MNIST handwritten dataset, achieving a test set accuracy of 98%, which demonstrates the effectiveness of CNNs in image recognition tasks. Finally, the feasibility and limitations of using advanced methods, such as residual networks (ResNet) and batch normalization, to address more complex image recognition problems are discussed.