A Robust Approach for Licence Plate Detection Using Deep Learning

Authors

  • Shefali Arora National Institute of Technology Jalandhar, India
  • Ruchi Mittal DataTech Department,Iconic Data, Tokyo, Japan
  • Dhruv Arora Purdue University, West Lafayette, Indiana, United States of America
  • Avinash Kumar Shrivastava International Management Institute Kolkata, India

DOI:

https://doi.org/10.4114/intartif.vol27iss73pp129-141

Keywords:

Vehicle plates, CNN, Deep Learning, localization

Abstract

Intelligent transport systems must be developed due to the rising use of vehicles, particularly cars. In the field of computer vision, the identification of a vehicle's licence plate (LP) has been crucial. Various methods and algorithms have been used for the detection process. It becomes challenging to find similar photos, nevertheless, because the features of these plates change depending on colour, font, and language of characters. The research proposes a powerful deep learning framework based on feature extraction using convolutional neural networks and localization using canny-edge detection. Three steps make up the model's operation. An improved approach integrating the usage of bilateral filters and Canny edge detection is used for the processes of segmentation and localization. Further, a CNN architecture is used to extract features from images and classify the presence of licence plates in unseen vehicles. If present, the stage is followed by recognition of numbers written on the plates. An extensive experimental investigation takes place using three datasets namely Stanford Cars, Car Licence Plate Detection dataset and Indian Licence Plates Database. The attained simulation outcome ensures a superior performance over existing techniques in a significant way.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Downloads

Published

2024-02-14

How to Cite

Arora, S., Mittal, R., Arora, D., & Shrivastava, A. K. (2024). A Robust Approach for Licence Plate Detection Using Deep Learning. Inteligencia Artificial, 27(73), 129–141. https://doi.org/10.4114/intartif.vol27iss73pp129-141