Asynchronous Federated Learning for Real-time Multiple Licence Plate Recognition through Semantic Communication

Real-time License Plate Recognition plays a significant role in traffic congestion control and road safety monitoring. Practically, a network camera may capture multiple license plates in one frame while the data collected by different network cameras cannot be shared due to privacy concern. In this paper, a federated learning framework is introduced to simultaneously detect multiple license plates over different network cameras through semantic communication. Specifically, to achieve a high efficiency of multiple license plates recognition in real time, the semantic segmentation model is applied to locally extract the important features of an image with multiple license plates. And then, an autoencoder is developed to carry out the semantic encoding which translates the meaningful information. Moreover, a multi-task learning approach for multiple license plates recognition is proposed through a multi-objective optimization technique which can train the license plate recognition model with stronger generalization. To improve the reliability, an asynchronous federated learning algorithm is also considered to ensure the training process can be tolerant to the transmission delay. Empirical experiments on the Chinese City Parking Dataset (CCPD) show that the proposed approach can effectively improve the recognition performance while providing robust service.