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License plate recognition system for complex scenarios based on improved YOLOv5s and LPRNet.

Xuanhong Wang1, Mingchen Wang1, Hongyu Guo1

  • 1School of Communication and Information Engineering, Xi'an University of Posts and Telecomunications, Xi'an, 710121, China.

Scientific Reports
|October 6, 2025
PubMed
Summary

This study introduces an improved license plate recognition (LPR) system using YOLOv5s and LPRNet, enhancing accuracy in complex conditions and enabling deployment on mobile devices.

Keywords:
LPRNetLicense plate recognitionObject detectionTriplet attentionYOLOv5s

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional license plate recognition (LPR) algorithms struggle with accuracy in challenging environments like adverse weather and plate tilt.
  • Deployment of LPR systems is often difficult due to hardware limitations.

Purpose of the Study:

  • To develop a lightweight, end-to-end method for license plate detection and recognition.
  • To improve the robustness and accuracy of LPR in complex scenarios.
  • To enable LPR deployment on resource-constrained platforms.

Main Methods:

  • Integrated an improved YOLOv5s with LPRNet, incorporating Triplet Attention for enhanced feature extraction.
  • Introduced Soft-NMS for robust detection of overlapping license plates.
  • Implemented a Spatial Transformer Network (STN) for geometric correction of tilted license plates.

Main Results:

  • Achieved 98.9% detection precision and 91.5% recognition accuracy on the CCPD2019 dataset.
  • Demonstrated significant improvements over the baseline YOLOv5s + LPRNet.
  • The model boasts 7.5M parameters and 18.1 GFLOPs, running at 147 FPS for detection.

Conclusions:

  • The proposed lightweight LPR method significantly enhances detection and recognition accuracy in complex conditions.
  • The system's efficiency and low resource requirements make it suitable for mobile and embedded applications.