Next Article in Journal
Using Reflection Symmetry to Improve the Protection of Radio-Electronic Equipment from Ultrashort Pulses
Previous Article in Journal
Koszulity and Point Modules of Finitely Semi-Graded Rings and Algebras
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle

Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations

Department of Computer and Software, Sejong Cyber University, Seoul 04992, Korea
Symmetry 2019, 11(7), 882; https://doi.org/10.3390/sym11070882
Received: 25 May 2019 / Revised: 13 June 2019 / Accepted: 24 June 2019 / Published: 5 July 2019
  |  
PDF [7422 KB, uploaded 5 July 2019]
  |  

Abstract

The number and range of the candidate vehicle license plate (VLP) region affects the result of the VLP extraction symmetrically. Therefore, in order to improve the VLP extraction rate, many candidate VLP regions are selected. However, there is a problem that the processing time increases symmetrically. In this paper, we propose a method that allows detecting a vehicle license plate in the real-time mode. To do this, the proposed method makes use of the region-based convolutional neural network (R-CNN) method and morphological operations. The R-CNN method is a deep learning method that selects a large number of candidate regions from an input image and compares them to determine whether objects of interest are included. However, this method has limitations when used in real-time processing. Therefore, to address this limitation in the proposed method, while selecting a candidate vehicle region, the selection range is reduced based on the size and position of the vehicle in the input image; hence, processing can be performed quickly. A vehicle license plate is detected by performing a morphological operation based on the edge pixel distribution of the detected vehicle region. Experimental results show that the detection rate of vehicles is approximately 92% in real road environments, and the detection rate of vehicle license plates is approximately 83%. View Full-Text
Keywords: vehicle detection; license plate extraction; region-based convolutional neural networks (R-CNN); morphological operations; advanced driver assistance system (ADAS) vehicle detection; license plate extraction; region-based convolutional neural networks (R-CNN); morphological operations; advanced driver assistance system (ADAS)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Kim, J. Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations. Symmetry 2019, 11, 882.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top