PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated Segmentation Learning
This addresses privacy and efficiency issues in license plate detection for transportation applications, but it is incremental as it adapts existing methods (U-Net and FL) to a specific domain.
The paper tackles the problem of license plate detection in intelligent transport systems by introducing PlateSegFL, which combines U-Net-based segmentation with Federated Learning to improve performance and preserve privacy, achieving around 95% F1 score.
Automatic License Plate Recognition (ALPR) is an integral component of an intelligent transport system with extensive applications in secure transportation, vehicle-to-vehicle communication, stolen vehicles detection, traffic violations, and traffic flow management. The existing license plate detection system focuses on one-shot learners or pre-trained models that operate with a geometric bounding box, limiting the model's performance. Furthermore, continuous video data streams uploaded to the central server result in network and complexity issues. To combat this, PlateSegFL was introduced, which implements U-Net-based segmentation along with Federated Learning (FL). U-Net is well-suited for multi-class image segmentation tasks because it can analyze a large number of classes and generate a pixel-level segmentation map for each class. Federated Learning is used to reduce the quantity of data required while safeguarding the user's privacy. Different computing platforms, such as mobile phones, are able to collaborate on the development of a standard prediction model where it makes efficient use of one's time; incorporates more diverse data; delivers projections in real-time; and requires no physical effort from the user; resulting around 95% F1 score.