Enhanced Malaysian License Plate Recognition System Using an Improved YOLOv2 Model

Authors

  • Syafeeza A.R. Machine Learning and Signal Processing (MLSP) Research Group, Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka, 76100, Durian Tunggal, Melaka, Malaysia.
  • P. Marzuki Machine Learning and Signal Processing (MLSP) Research Group, Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka, 76100, Durian Tunggal, Melaka, Malaysia.
  • Asar Khan Machine Learning and Signal Processing (MLSP) Research Group, Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka, 76100, Durian Tunggal, Melaka, Malaysia.
  • Norihan Abdul Hamid Machine Learning and Signal Processing (MLSP) Research Group, Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka, 76100, Durian Tunggal, Melaka, Malaysia.
  • Wira Hidayat Mohd Saad Machine Learning and Signal Processing (MLSP) Research Group, Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer (FTKEK), Universiti Teknikal Malaysia Melaka, 76100, Durian Tunggal, Melaka, Malaysia.
  • Airuz Sazura A. Samad Venture GES Manufacturing Services (M) Sdn. Bhd, 81400, Kulai Jaya, Johor, Malaysia.

DOI:

https://doi.org/10.54554/jtec.2024.16.03.005

Keywords:

License Plate, Detection, Recognition, Preprocessing, Convolutional Neural Network, Malaysian License Plate Recognition

Abstract

License Plate Recognition (LPR) has gained popularity among researchers due to its wide range of applications, including law enforcement, monitoring, and toll gate systems. However, existing LPR systems still require improvements to achieve optimum accuracy and speed. The advancements in Convolutional Neural Network (CNN) variants offer potential solutions for these challenges. This primary aim of this system is to ensure accurate and efficient recognition of the vehicle plate characters using CNN techniques. This research utilizes two CNN network architectures for deep object detection to address the Malaysian License Plate Recognition (MLPR) task. The first network is designed to detect the license plate, while the second is responsible for recognizing the characters on the plate. Both networks are cascaded from the architecture of two-stage YOLOv2, providing promising speed and accuracy. The MLPR system achieved an accuracy of 98.75% and a processing speed of 0.0104 seconds, using a total of  2,200 license plate images. In conclusion, the system adapted from deep object detection techniques presents a promising solution for the MLPR problem, based on the achieved accuracy and speed.

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Published

2024-09-30

How to Cite

A.R. , S., Marzuki, P. ., Khan, A. ., Abdul Hamid, N. ., Mohd Saad, W. H., & A. Samad, A. S. . (2024). Enhanced Malaysian License Plate Recognition System Using an Improved YOLOv2 Model. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 16(3), 35–39. https://doi.org/10.54554/jtec.2024.16.03.005

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