A License Plate Recognition System for Highway Emergency Lane Monitoring Using YOLOv11-pose and PaddleOCR

Xiaonan Liang; Shu Li; Chenyan Fan; Xinyue Zhang

doi:10.47297/taposatWSP2633-456927.20250609

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A License Plate Recognition System for Highway Emergency Lane Monitoring Using YOLOv11-pose and PaddleOCR

6卷第9期

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A License Plate Recognition System for Highway Emergency Lane Monitoring Using YOLOv11-pose and PaddleOCR
A License Plate Recognition System for Highway Emergency Lane Monitoring Using YOLOv11-pose and PaddleOCR
School of Science, Xi'an Shiyou University, Xi'an, Shaanxi, 710065, China
2025年6卷第9期页码：136-140
纸质出版：2025-09-30
DOI：10.47297/taposatWSP2633-456927.20250609
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Xiaonan Liang, Shu Li, Chenyan Fan & Xinyue Zhang. (2025). A License Plate Recognition System for Highway Emergency Lane Monitoring Using YOLOv11-pose and PaddleOCR. 科技理论与实践（英文版）, 6(9), 136-140.

Liang Xiaonan,Li Shu,Fan Chenyan,et al.A License Plate Recognition System for Highway Emergency Lane Monitoring Using YOLOv11-pose and PaddleOCR[J].Theory and Practice of Science and Technology,2025,06(09):136-140.
Xiaonan Liang, Shu Li, Chenyan Fan & Xinyue Zhang. (2025). A License Plate Recognition System for Highway Emergency Lane Monitoring Using YOLOv11-pose and PaddleOCR. 科技理论与实践（英文版）, 6(9), 136-140. DOI： 10.47297/taposatWSP2633-456927.20250609.

Liang Xiaonan,Li Shu,Fan Chenyan,et al.A License Plate Recognition System for Highway Emergency Lane Monitoring Using YOLOv11-pose and PaddleOCR[J].Theory and Practice of Science and Technology,2025,06(09):136-140. DOI： 10.47297/taposatWSP2633-456927.20250609.

摘要

Abstract

Emergency lanes are critical for ensuring timely access of rescue vehicles during traffic congestion

significantly reducing accident fatalities when kept unobstructed. This study proposes a robust license plate recognition (LPR) system for monitoring illegal occupation of highway emergency lanes. The method integrates YOLOv11-pose for precise four-corner detection and PaddleOCR (PP-OCRv5) for high-speed character recognition

achieving an average recognition time of less than 10 milliseconds per plate. The models are unified via ONNX for efficient inference and evaluated on real-world surveillance videos from the Changshen Expressway. Experimental results demonstrate high accuracy and strong robustness

achieving 98.79% recognition accuracy and reliable performance across diverse scenarios

including new energy vehicle plates and long-distance captures. The system effectively supports automated traffic violation detection.

关键词

Keywords

references

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Advancing License Plate Recognition in Complex Environments: A Hybrid Approach Integrating Deep Learning and Image Pre-Processing

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