Deep learning in food category recognition

This paper proposes a deep learning-based food image recognition system to enhance checkout efficiency in university cafeterias. Addressing the bottlenecks of traditional manual checkout methods, a food image classification model has been designed and implemented, combining Convolutional Neural Networks (CNN) and Fully Connected Networks (FCN). The system extracts image features through convolutional layers, reduces feature map dimensions using pooling layers, and makes classification decisions through fully connected layers. Additionally, data augmentation techniques were utilized to expand the training data, and optimization methods such as regularization and Dropout were introduced to improve the model's generalization ability. The main structure of the paper includes sections on system architecture design, data preparation and processing, experimental results, and analysis. The system architecture design section details the construction methods of CNN and FCN and their application in this system. The data preparation and processing section describes the process of obtaining raw data, data augmentation, and preprocessing. The experimental results and analysis section demonstrates the impact of different parameter settings on model performance and proves the effectiveness of the proposed method through experimental data. The system is characterized by its efficient food image classification ability and good generalization performance. Extensive experiments on the dataset revealed that increasing the sample size and setting hyperparameters appropriately can significantly improve the model's accuracy and stability. With a sample size of 10,000, the model achieved an average accuracy of 86.88%, and the low standard deviation indicates stable performance in practical applications.