Food Image Classification and Food Safety Detection based on Pretrained Convolutional Neural Network

This study is based on a pre-trained Convolutional Neural Network (CNN) and focuses on the classification of food images and food safety detection. Firstly, a pre-trained CNN model is used to extract features from a large number of food images. Then, the model parameters are fine-tuned to optimize the classification performance. Next, a food safety detection system is constructed, which utilizes the CNN model to classify food images and assess and detect the quality and safety of food by comparing them with a food database. Experimental results demonstrate that the pre-trained CNN model achieves good performance in food image classification and food safety detection tasks. This method not only accurately classifies food images but also effectively detects potential food safety hazards, providing a feasible solution for quality control and food safety in the food industry. Despite achieving certain results in this study, there are still challenges and room for improvement. Future research can further optimize the architecture and parameters of the model to enhance the performance of classification and detection.