An End-to-End Automated License Plate Recognition System Using YOLO Based Vehicle and License Plate Detection with Vehicle Classification

Reda Al-batat, Anastassia Angelopoulou, Smera Premkumar, Jude Hemanth, Epameinondas Kapetanios, Francisco J. Martinez (Editor)

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Abstract

An accurate and robust Automatic License Plate Recognition (ALPR) method proves surprising versatility in an Intelligent Transportation and Surveillance (ITS) system. However, most of the existing approaches often use prior knowledge or fixed pre-and-post processing rules and are thus limited by poor generalization in complex real-life conditions. In this paper, we leverage a YOLO-based end-to-end generic ALPR pipeline for vehicle detection (VD), license plate (LP) detection and recognition without exploiting prior knowledge or additional steps in inference. We assess the whole ALPR pipeline, starting from vehicle detection to the LP recognition stage, including a vehicle classifier for emergency vehicles and heavy trucks. We used YOLO v2 in the initial stage of the pipeline and remaining stages are based on the state-of-the-art YOLO v4 detector with various data augmentation and generation techniques to obtain LP recognition accuracy on par with current proposed methods. To evaluate our approach, we used five public datasets from different regions, and we achieved an average recognition accuracy of 90.3% while maintaining an acceptable frames per second (FPS) on a low-end GPU.
Original languageEnglish
Article number9477
Number of pages17
JournalSensors
Volume22
Issue number23
Early online date4 Dec 2022
DOIs
Publication statusPublished - 4 Dec 2022

Keywords

  • Article
  • automatic license plate recognition
  • convolutional neural networks
  • YOLO
  • Intelligence
  • Recognition, Psychology
  • Ambulances
  • Knowledge
  • Bone Plates

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