Unified Chinese License Plate Detection and Recognition with High Efficiency
This work addresses the problem of robust license plate recognition for Chinese systems, but it is incremental as it builds on existing deep learning methods with a new dataset.
The authors tackled the lack of large datasets for Chinese license plate detection and recognition by introducing the Chinese Road Plate Dataset (CRPD), and they developed a unified network that achieves real-time inference at 30 fps with competitive performance on public benchmarks.
Recently, deep learning-based methods have reached an excellent performance on License Plate (LP) detection and recognition tasks. However, it is still challenging to build a robust model for Chinese LPs since there are not enough large and representative datasets. In this work, we propose a new dataset named Chinese Road Plate Dataset (CRPD) that contains multi-objective Chinese LP images as a supplement to the existing public benchmarks. The images are mainly captured with electronic monitoring systems with detailed annotations. To our knowledge, CRPD is the largest public multi-objective Chinese LP dataset with annotations of vertices. With CRPD, a unified detection and recognition network with high efficiency is presented as the baseline. The network is end-to-end trainable with totally real-time inference efficiency (30 fps with 640p). The experiments on several public benchmarks demonstrate that our method has reached competitive performance. The code and dataset will be publicly available at https://github.com/yxgong0/CRPD.