Robust License Plate Detection using Convolutional Neural Network

DOI

10.2991/icst-18.2018.117How to use a DOI?

Keywords

license plate, detection, CNN, mobile device

Abstract

In general, the vehicle number plate detection system in an image should be able to overcome two problems, first how to determine the position of the vehicle number plate or which vehicle number plate is on an image and the second how big is the plate. A number of vehicle license plate detection methods have been proposed over the past two decades, and some have shown success in certain tasks. This study will detect the license plate number of vehicles using the CNN. The initial process is to create a training data license plate numbers using CNN processed on the server. Furthermore, the training data entered on the vehicle license plate detection applications. Vehicle license plate detection results will be displayed in percent accuracy. Based on the testing, the results obtained were very satisfactory with the accuracy of detection of vehicle license numbers as much as 98.19%, the remaining 1.81% of the plates were detected but not the image of the plate that was cropped. It shows that the method has been implemented on CNN that training data and license plate detection is very pretty accurate in detecting vehicle license plate. Implementation of CNN methods on android based mobile devices for data processing training and detection testing of vehicle license plate shows very satisfactory results.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - I Nyoman Gede Arya Astawa
AU  - I Gusti Ngurah Bagus Caturbawa
AU  - Elina Rudiastari
AU  - I Made Ari Dwi Suta Atmaja
PY  - 2018/12
DA  - 2018/12
TI  - Robust License Plate Detection using Convolutional Neural Network
BT  - Proceedings of the International Conference on Science and Technology (ICST 2018)
PB  - Atlantis Press
SP  - 564
EP  - 567
SN  - 2589-4943
UR  - https://doi.org/10.2991/icst-18.2018.117
DO  - 10.2991/icst-18.2018.117
ID  - Astawa2018/12
ER  -