Developing a highly accurate automatic license plate recognition system (ALPR) is challenging due to environmental factors such as lighting, rain, and dust. Additional difficulties include high vehicle speeds, varying camera angles, and low-quality or low-resolution images. ALPR is vital in traffic control, parking, vehicle tracking, toll collection, and law enforcement applications. This paper proposes a deep learning strategy using YOLOv8 for license plate detection and recognition tasks. This method seeks to enhance the performance of the model using datasets from Ontario, Quebec, California, and New York State. It achieved an impressive recall rate of 94% on the dataset from the Center for Pattern Recognition and Machine Intelligence (CENPARMI) and 91% on the UFPR-ALPR dataset. In addition, our method follows a semi-supervised learning framework, combining a small set of manually labeled data with pseudo-labels generated by Grounding DINO to train our detection model. Grounding DINO, a powerful vision-language model, automatically annotates many images with bounding boxes for license plates, thereby minimizing the reliance on labor-intensive manual labeling. By integrating human-verified and model-generated annotations, we can scale our dataset efficiently while maintaining label quality, which significantly enhances the training process and overall model performance. Furthermore, it reports character error rates for both datasets, providing additional insight into system performance.

翻译：由于光照、雨雾、灰尘等环境因素，开发高精度的自动车牌识别系统（ALPR）具有挑战性。其他困难还包括车辆高速行驶、摄像头角度多变以及图像质量或分辨率较低。ALPR在交通管控、停车管理、车辆追踪、收费系统及执法应用中至关重要。本文提出一种基于YOLOv8的深度学习策略，用于车牌检测与识别任务。该方法利用来自安大略省、魁北克省、加利福尼亚州和纽约州的数据集以提升模型性能。在模式识别与机器智能中心（CENPARMI）数据集上实现了94%的召回率，在UFPR-ALPR数据集上达到91%。此外，本方法采用半监督学习框架，将少量人工标注数据与Grounding DINO生成的伪标签相结合来训练检测模型。Grounding DINO作为一种强大的视觉-语言模型，能自动为大量图像生成车牌边界框标注，从而显著减少对人工标注的依赖。通过整合人工验证与模型生成的标注，我们能在保持标签质量的同时高效扩展数据集，这显著优化了训练过程并提升了整体模型性能。研究还报告了两个数据集的字符错误率，为系统性能评估提供了更深入的视角。