Abstract
Under natural conditions, license plate recognition is easily affected by factors such as lighting and shooting angles. Given the diverse types of Chinese license plates and the intricate structure of Chinese characters compared to Latin characters, accurate recognition of Chinese license plates poses a significant challenge. To address this issue, we introduce a novel Chinese License Plate Transformer (CLPT). In CLPT, license plate images pass through a Transformer encoder, and the resulting Tokens are divided into four categories via an Auto Token Classify (ATC) mechanism. These categories include province, main, suffix, and noise. The first three categories serve to predict the respective parts of the license plate - the province, main body, and suffix. In our tests, we employed YOLOv8-pose as the license plate detector, which excels in detecting both bounding boxes and key points, aiding in the correction of perspective transformation in distorted license plates. Experimental results on the CCPD, CLPD, and CBLPRD datasets demonstrate the superior performance of our method in recognizing both single-row and double-row license plates. We achieved an accuracy rate of 99.6%, 99.5%, and 89.3% on the CCPD Tilt, Rotate, and Challenge subsets, respectively. In addition, our method attained an accuracy of 87.7% in the CLPD and 99.9% in the CBLPRD, maintaining an impressive 99.5% accuracy even for yellow double-row license plates in the CBLPRD.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOV4: optimal speed and accuracy of object detection. arXiv preprint: arXiv:2004.10934 (2020)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint: arXiv:2010.11929 (2020)
Gong, Y., et al.: Unified Chinese license plate detection and recognition with high efficiency. J. Vis. Commun. Image Represent. 86, 103541 (2022)
Li, H., Wang, P., Shen, C.: Toward end-to-end car license plate detection and recognition with deep neural networks. IEEE Trans. Intell. Transp. Syst. 20(3), 1126–1136 (2018)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Mehta, S., Rastegari, M.: MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint: arXiv:2110.02178 (2021)
Raj, S., Gupta, Y., Malhotra, R.: License plate recognition system using yolov5 and CNN. In: 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1, pp. 372–377. IEEE (2022)
Redmon, J., Farhadi, A.: YOLOV3: an incremental improvement. arXiv preprint: arXiv:1804.02767 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347–10357. PMLR (2021)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wang, P., Da, C., Yao, C.: Multi-granularity prediction for scene text recognition. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. Lecture Notes in Computer Science, vol. 13688, pp. 339–355. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19815-1_20
Wang, Y., Bian, Z.P., Zhou, Y., Chau, L.P.: Rethinking and designing a high-performing automatic license plate recognition approach. IEEE Trans. Intell. Transp. Syst. 23(7), 8868–8880 (2021)
Wu, K., et al.: TinyViT: fast pretraining distillation for small vision transformers. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. Lecture Notes in Computer Science, vol. 13681, pp. 68–85. Springer, Cham (2022)
Xu, Z., et al.: Towards end-to-end license plate detection and recognition: a large dataset and baseline. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 261–277. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_16
Zhang, L., Wang, P., Li, H., Li, Z., Shen, C., Zhang, Y.: A robust attentional framework for license plate recognition in the wild. IEEE Trans. Intell. Transp. Syst. 22(11), 6967–6976 (2020)
Zou, Y., et al.: License plate detection and recognition based on YOLOV3 and ILPRNET. SIViP 16(2), 473–480 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xia, R., Song, W., Liu, X., Zhao, X. (2024). Tripartite Architecture License Plate Recognition Based on Transformer. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14426. Springer, Singapore. https://doi.org/10.1007/978-981-99-8432-9_33
Download citation
DOI: https://doi.org/10.1007/978-981-99-8432-9_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8431-2
Online ISBN: 978-981-99-8432-9
eBook Packages: Computer ScienceComputer Science (R0)