Software for improving the quality of license plate recognition based on super-resolution neural network models

Recognition of license plates (LP) is one of the key tasks for intelligent transport systems. In practice, such factors as blur, noise, adverse weather conditions or shooting from a long distance lead to obtaining low-resolution (LR) images, which significantly reduces the reliability of recognition. A promising solution to this problem is the use of super-resolution (SR) methods capable of restoring high-resolution (HR) images from the corresponding LR versions. This paper is devoted to the research and development of a software package using neural network super-resolution models to improve the quality and accuracy of LP recognition. The software package implements the YOLO (You Only Look Once) neural network architectures for object detection, the SORT (Simple Online and Realtime Tracking) object tracking algorithm and super-resolution models to improve LP images. This approach ensures high accuracy of LP recognition even when working with images obtained in difficult shooting conditions characterized by low quality or resolution. The experimental results demonstrate that the proposed approach can improve the accuracy of LP recognition in low-resolution images. The image restoration quality was assessed using the PSNR and SSIM metrics, which confirmed the improvement of the visual characteristics of LP for the most effective models. The developed software package has a wide potential for practical application and can be integrated into various systems, for example, for access control to protected areas, traffic monitoring and analysis, automation of parking complexes, as well as as part of solutions for ensuring public safety. The flexibility of the implemented architecture allows you to adapt the system to specific requirements with modifications, which emphasizes its versatility and practical significance.

1. Vašek V., Franc V., Urban M. License Plate Recognition and Super-Resolution from Low-Resolution Videos by Convolutional Neural Networks. In: BMVC 2018: Proceedings of British Machine Vision Conference, 03–06 September 2018, Newcastle, UK. BMVA Press; 2018. URL: http://bmvc2018.org/contents/papers/0537.pdf

2. Tian Yu., Ye Q., Doermann D. YOLOv12: Attention-Centric Real-Time Object Detectors. arXiv. URL: https://arxiv.org/abs/2502.12524 [Accessed 15th June 2025].

3. Jaderberg M., Simonyan K., Zisserman A., Kavukcuoglu K. Spatial Transformer Networks. In: Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 07–12 December 2015, Montreal, QC, Canada. 2015. P. 2017–2025.

4. Zherzdev S., Gruzdev A. LPRNet: License Plate Recognition via Deep Neural Networks. arXiv. URL: https://arxiv.org/abs/1806.10447 [Accessed 15th June 2025].

5. Ledig Ch., Theis L., Huszár F., et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21–26 July 2017, Honolulu, HI, USA. IEEE; 2017. P. 105–114. https://doi.org/10.1109/CVPR.2017.19

6. Wang X., Yu K., Wu Sh., et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. In: Computer Vision – ECCV 2018 Workshops: Proceedings: Part V, 08–14 September 2018, Munich, Germany. Cham: Springer; 2018. P. 63–79. https://doi.org/10.1007/978-3-030-11021-5_5

7. Zhang K., Liang J., Van Gool L., Timofte R. Designing a Practical Degradation Model for Deep Blind Image Super-Resolution. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 10–17 October 2021, Montreal, QC, Canada. IEEE; 2021. P. 4771–4780. https://doi.org/10.1109/ICCV48922.2021.00475

8. Soviany P., Ardei C., Ionescu R.T., Leordeanu M. Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN). In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 01–05 March 2020, Snowmass, CO, USA. IEEE; 2020. P. 3452–3461. https://doi.org/10.1109/WACV45572.2020.9093408

9. Dosovitskiy A., Beyer L., Kolesnikov A., et al. An Image Is Worth 16×16 Words: Transformers for Image Recognition at Scale. In: ICLR 2021: 9th International Conference on Learning Representations, 03–07 May 2021, Virtual Event, Austria. 2021. URL: https://arxiv.org/abs/2010.11929

10. Bu Q., Park S., Khang M., Cheng Yi. SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression. In: Thirty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, 20–27 February 2024, Vancouver, Canada. AAAI Press; 2024. P. 855–863.

11. Chen X., Wang X., Zhou J., Qiao Yu, Dong Ch. Activating More Pixels in Image Super-Resolution Transformer. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 17–24 June 2023, Vancouver, BC, Canada. IEEE; 2023. P. 22367–22377. https://doi.org/10.1109/CVPR52729.2023.02142

12. Mou Ch., Wu Ya., Wang X., Dong Ch., Zhang J., Shan Yi. Metric Learning Based Interactive Modulation for Real-World Super-Resolution. In: Computer Vision – ECCV 2022: 17th European Conference: Proceedings: Part XVII, 23–27 October 2022, Tel Aviv, Israel. Cham: Springer; 2022. P. 723–740. https://doi.org/10.1007/978-3-031-19790-1_43

13. Liu K., Jiang Yi., Choi I., Gu J. Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration. In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 01–06 October 2023, Paris, France. IEEE; 2023. P. 5350–5360. https://doi.org/10.1109/ICCV51070.2023.00495