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.

自动车牌识别（ALPR）是智能交通系统的一个重要组成部分，在安全交通、车辆间通信、盗车探测、交通违规和交通流管理等方面有广泛应用。该研究论文提出了PlateSegFL，引入了基于U-Net的分割和联合学习（FL），通过适合于多类别图像分割任务的U-Net和保护用户隐私的联合学习，有效解决了现有车牌检测系统的限制性问题，提升了模型性能。该系统能够在多个计算平台上协同开发标准预测模型，从而充分利用时间和各种数据，提供实时预测结果，并且不需要用户付出实际努力，达到了大约95％的F1分数。

PlateSegFL: 一种使用联合分割学习的隐私保护车牌检测方法