PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated
  Segmentation Learning
Md. Shahriar Rahman Anuvab Mishkat Sultana Md. Atif Hossain Shashwata Das Suvarthi Chowdhury Rafeed Rahman Dibyo Fabian Dofadar Shahriar Rahman Rana
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.