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

• URL: http://arxiv.org/abs/2510.25032v1
• Date: Tue, 28 Oct 2025 23:21:00 GMT
• Title: Efficient License Plate Recognition via Pseudo-Labeled Supervision with Grounding DINO and YOLOv8
• Authors: Zahra Ebrahimi Vargoorani, Amir Mohammad Ghoreyshi, Ching Yee Suen,
• Abstract summary: ALPR is vital in traffic control, parking, vehicle tracking, toll collection, and law enforcement applications.<n>This paper proposes a deep learning strategy using YOLOv8 for license plate detection and recognition tasks.<n>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.
• Score: 0.29949629644252374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition [1.6566053195631465]
  In this paper, a selective Generative Adversarial Network (GAN) is proposed for deblurring in the preprocessing step.<n>YOLOv5 achieves a detection time of 0.026 seconds for both License-Plate Detection (LPD) and Character Recognition (CR) detection stages.<n>The proposed model achieves accuracy rates of 95% and 97% in the LPD and CR detection phases, respectively.
  arXiv  Detail & Related papers  (2025-09-08T16:34:54Z)
• Connecting Vision and Emissions: A Behavioural AI Approach to Carbon Estimation in Road Design [0.0]
  We present an enhanced YOLOv8 real time vehicle detection and classification framework, for estimating carbon emissions in urban environments.<n>The framework incorporates a hybrid pipeline where each detected vehicle is tracked and its bounding box is cropped and passed to a deep Optical Character Recognition (OCR) module.<n>This OCR system, composed of multiple convolutional neural network (CNN) layers, is trained specifically for character-level detection and license plate decoding.
  arXiv  Detail & Related papers  (2025-06-18T11:50:24Z)
• License Plate Detection and Character Recognition Using Deep Learning and Font Evaluation [0.6215404942415159]
  This paper proposes a dual deep learning strategy using a Faster R-CNN for detection and a CNN-RNN model with Connectionist Temporal Classification (CTC) loss and a MobileNet V3 backbone for recognition.<n>The research examines the role of font features in license plate (LP) recognition, analyzing fonts like Driver Gothic, Dreadnought, California Clarendon, and Zurich Extra Condensed with the OpenALPR system.
  arXiv  Detail & Related papers  (2024-12-17T06:03:42Z)
• TrajSSL: Trajectory-Enhanced Semi-Supervised 3D Object Detection [59.498894868956306]
  Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student framework. We leverage pre-trained motion-forecasting models to generate object trajectories on pseudo-labeled data. Our approach improves pseudo-label quality in two distinct manners.
  arXiv  Detail & Related papers  (2024-09-17T05:35:00Z)
• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• A High-Accuracy Unsupervised Person Re-identification Method Using Auxiliary Information Mined from Datasets [53.047542904329866]
  We make use of auxiliary information mined from datasets for multi-modal feature learning. This paper proposes three effective training tricks, including Restricted Label Smoothing Cross Entropy Loss (RLSCE), Weight Adaptive Triplet Loss (WATL) and Dynamic Training Iterations (DTI)
  arXiv  Detail & Related papers  (2022-05-06T10:16:18Z)
• End-to-End High Accuracy License Plate Recognition Based on Depthwise Separable Convolution Networks [0.0]
  We propose a novel segmentation-free framework for license plate recognition and introduce NP-ALPR dataset. The proposed network model consists of the latest deep learning methods and state-of-the-art ideas, and benefits from a novel network architecture. We evaluate the effectiveness of the proposed method on three different datasets and show a recognition accuracy of over 99% and over 70 fps.
  arXiv  Detail & Related papers  (2022-02-21T14:45:03Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• A Robust Attentional Framework for License Plate Recognition in the Wild [95.7296788722492]
  We propose a robust framework for license plate recognition in the wild. It is composed of a tailored CycleGAN model for license plate image generation and an elaborate designed image-to-sequence network for plate recognition. We release a new license plate dataset, named "CLPD", with 1200 images from all 31 provinces in mainland China.
  arXiv  Detail & Related papers  (2020-06-06T17:11:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.