Efficient License Plate Recognition via Pseudo-Labeled Supervision with Grounding DINO and YOLOv8

TL;DR

This paper introduces a semi-supervised license plate recognition system that combines Grounding DINO and YOLOv8, achieving high accuracy and reducing manual labeling efforts through pseudo-labeling.

Contribution

It presents a novel semi-supervised learning approach using pseudo-labels from Grounding DINO to improve license plate detection and recognition performance.

Findings

94% recall on CENPARMI dataset

91% recall on UFPR-ALPR dataset

Character error rates provided for performance assessment

Abstract

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.