Robust License Plate Detection using Convolutional Neural Network
I Nyoman Gede Arya Astawa, I Gusti Ngurah Bagus Caturbawa, Elina Rudiastari, I Made Ari Dwi Suta Atmaja
I Nyoman Gede Arya Astawa
Available Online December 2018.
- https://doi.org/10.2991/icst-18.2018.117How to use a DOI?
- license plate, detection, CNN, mobile device
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/icst-18.2018.117 ID - Astawa2018/12 ER -