Urban Intelligent Transportation-Oriented License Plate Recognition Model for Severe Environments Based on Hybrid Architecture of YOLOv12, GAN and Mamba-SSM

Adverse weather and low-illumination conditions in urban road scenarios substantially degrade license plate image quality, posing a major challenge to robust automatic license plate recognition for urban intelligent transportation systems and smart city construction. To address the limitations of conventional pipelines that optimize detection, enhancement, and recognition in isolation, this study proposes CLEI, a unified framework integrating YOLOv12-based detection, GAN-based image enhancement, and a novel CNN–Mamba network (CMN) for character recognition. Using a curated dataset of 3000 license plate images captured under rain, snow, fog, and nighttime urban roadside conditions, we first benchmarked several mainstream detectors and identified YOLOv12s as the most effective model in terms of accuracy, inference speed, and computational efficiency. To mitigate blur and low-quality degradation in cropped plate regions, DeblurGAN-v2 was employed for adaptive enhancement, achieving PSNR of 16.61 dB, SSIM of 0.8776, and LPIPS of 0.1151. For recognition, the proposed CMN replaces the recurrent module in CRNN with a Mamba-based state-space model, improving sequence modeling efficiency and robustness. CMN achieved 93.3% plate accuracy, outperforming CRNN (91.0%) and LPRNet (88.5%), while the full CLEI framework reached 93.67% accuracy after enhancement. These results demonstrate that collaborative optimization across detection, restoration, and recognition enables accurate and efficient license plate recognition in severely degraded urban traffic environments, providing a reliable technical support for urban traffic monitoring, public security governance and smart city infrastructure construction.