Multimodal Neural Network Optimization Methods in RFID Identification

In this study, we discuss the optimization strategy of applying multimodal neural networks in RFID identification scenarios. By combining visual information and RFID data, a Joint Convolutional Neural Network (Joint CNN) model is proposed to achieve more accurate target recognition. We have optimized the model structure and enhanced the feature fusion process to improve the recognition performance. At the same time, transfer learning technology was used to further improve the classification effect under small sample conditions. The experimental results show that the multimodal method significantly improves the accuracy of RFID identification, indicating that the combined use of different modal data can help solve the challenges in traditional RFID identification.