Research Article
HomoNet: Unified License Plate Detection and Recognition in Complex Scenes
@INPROCEEDINGS{10.1007/978-3-030-67540-0_16, author={Yuxin Yang and Wei Xi and Chenkai Zhu and Yihan Zhao}, title={HomoNet: Unified License Plate Detection and Recognition in Complex Scenes}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={License plate Keypoints location HomoPooling}, doi={10.1007/978-3-030-67540-0_16} }- Yuxin Yang
Wei Xi
Chenkai Zhu
Yihan Zhao
Year: 2021
HomoNet: Unified License Plate Detection and Recognition in Complex Scenes
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_16
Abstract
Although there are many commercial systems for license plate detection and recognition (LPDR), existing approaches based on object detection and Optical Character Recognition (OCR) are difficult to achieve good performance in both efficiency and accuracy in complex scenes (e.g., varying viewpoint, light, weather condition, etc). To tackle this problem, this work proposed a unified end-to-end trainable fast perspective LPDR network named HomoNet for simultaneous detection and recognition of twisted license plates. Specifically, we state the homography pooling (HomoPooling) operation based on perspective transformation to rectify tilted license plates. License plate detection was replaced with keypoints location to obtain richer information and improve the speed and accuracy. Experiments show that our network outperforms the state-of-the-art methods on public datasets, such as 95.58%@22.5 ms on RP and 97.5%@19 ms on CCPD.
