PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated Segmentation Learning

TL;DR

PlateSegFL introduces a privacy-preserving federated segmentation approach for license plate detection using U-Net, achieving high accuracy while addressing network and privacy issues in video data streams.

Contribution

It combines U-Net segmentation with federated learning to enhance license plate detection performance and privacy preservation in a distributed setting.

Findings

Achieved around 95% F1 score in license plate detection.

Reduced data transfer and privacy risks through federated learning.

Effective collaboration across devices like mobile phones.

Abstract

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.