License Plate Recognition Using Federated Learning

Federated learning is a decentralized approach in machine learning where models are trained locally on distributed data across various devices or servers. Only model updates, not the raw data, are sent to a central server for aggregation, ensuring privacy and security. This method supports collaborative learning without centralizing sensitive information. Vehicle plate images are gathered, and a Federated Learning algorithm processes them. The primary aim of this study is to improve the privacy of License Plate Recognition systems. The rationale for using License Plate Recognition technology lies in its potential to enhance security through access control and prevent unauthorized entries. Additionally, License Plate Recognition helps in the quick identification of stolen vehicles, leading to prompt response actions, and enhances traffic flow, reducing congestion and optimizing efficiency. Federated Learning ensures secure data transmission, strengthening system reliability. The methodology includes collecting standardized image data from cameras, enhancing image quality, and extracting alphanumeric characters from license plates. The Federated Learning algorithm maintains data decentralization, boosting privacy. A thorough evaluation, adherence to data protection regulations, and comprehensive documentation ensure system accuracy and transparency. Expected results suggest this integrated system offers a comprehensive solution to urban traffic issues. By utilizing image-based technology, it gathers essential data, improving traffic management efficiency and cost-effectiveness. The License Plate Recognition algorithm effectively detects license plates. Overall, this project presents a holistic approach to enhancing road safety and traffic management. By focusing on license plate detection, it addresses key urban traffic challenges. The integration of segmentation and advanced algorithms aims to create safer roadways and improve the well-being of urban communities.