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Smart city article details
| Title | Research On A Personalized License Plate Recognition Algorithm Based On The Lprnet Model |
|---|---|
| ID_Doc | 45250 |
| Authors | Han R.; Sun S. |
| Year | 2025 |
| Published | 2025 IEEE 17th International Conference on Computer Research and Development, ICCRD 2025 |
| DOI | http://dx.doi.org/10.1109/ICCRD64588.2025.10963078 |
| Abstract | Automatic recognition of vehicle license plates has become a core issue in the construction and management of smart cities and intelligent transportation systems both domestically and internationally. Core recognition algorithms face significant challenges due to varying lighting conditions, diverse background colors, personalized license plates, and the visual similarity of characters. This paper proposes an improved recognition model based on the LPRNet model-SE-Bi-LPRNet. This model employs YOLOv8 for precise localization of the license plate area and introduces an attention layer that integrates attention and contextual mechanisms. By incorporating a bidirectional long-short-Term gated algorithm and threshold selection, it effectively addresses complex background environments. The attention layer enhances the recognition accuracy of visually similar characters, while the bidirectional gating algorithm improves adaptability in complex scenes. Experimental results show that SE-Bi-LPRNet achieves a recognition accuracy of 96.79% in complex environments and 95.32% in personalized character recognition, significantly outperforming traditional LPRNet and other methods while maintaining competitive processing times. These improvements highlight the robustness and application potential of the model in intelligent transportation systems. Future research will explore integrating Transformer encoders into the model to enhance sequence modeling capabilities and overall performance. © 2025 IEEE. |
| Author Keywords | Complex Environments; Image Recognition; Intelligent Transportation; License Plate Recognition; LPRNet |
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