Designing Automatic Number Plate Recognition (ANPR) Systems Based on K-NN Machine Learning on the Raspberry Pi Embedded System

Sugeng Sugeng(1), Eniman Yunus Syamsuddin(2),
(1)   Indonesia
(2)   Indonesia

Corresponding Author


DOI : https://doi.org/10.24036/jtev.v5i1.1.106135

Full Text:    Language : en

Abstract


Research on vehicle number plate recognition or Automatic Number Plate Recognition (ANPR) is mostly done by researchers to produce an introduction that has high accuracy. Several methods of introduction are carried out such as introduction to edge detection and morphology, relationship analysis between objects, machine learning and deep learning. In this research a K-NN machine learning ANPR system was developed in character recognition. The method of analyzing relationships between objects is used to localize number plates. The system that was developed also added an artificial intelligence to be able to find out the fault of the number plate recognition and fix it based on the position of the character group in the number plate. The ANPR system is designed to be an Embedded system so that it can be implemented to be able to carry out the identification of two-wheeled and four-wheeled vehicle license plates. The ANPR system was also developed to be used in the parking management system. In this research the recognized number plates are limited to private number plates in Indonesia. In testing, the system is made capable of recognizing the number plates of two-wheeled vehicles and four-wheeled vehicles on vehicles that have a standard license plate according to Polri regulations, both in the font type and the number plate writing format. The results of vehicle number plate recognition reached an accuracy of 98%.

Keywords


ANPR; K-NN; Connected Component; Embedded System

References


B.Pechiammal. 2017. “An Efficient Approach For Automatic License Plate.” IEEE Science Technology Engineering & Management, 121–29.

Budianto, Aris. 2018. “Automatic License Plate Recognition : A Review with Indonesian Case Study” 5 (2): 258–70.

Chen, Rongbao, and Yunfei Luo. 2012. “Physics Procedia Detection” 24: 1350–56. https://doi.org/10.1016/j.phpro.2012.02.201.

Du, Shan, Mahmoud Ibrahim, Mohamed Shehata, and Senior Member. 2013. “Automatic License Plate Recognition ( ALPR ): A State-of-the-Art Review.” IEEE Transactions on Circuits and Systems for Video Technology 23 (2): 311–25. https://doi.org/10.1109/TCSVT.2012.2203741.

Fomani, Babak Abad. 2017. “License Plate Detection Using Adaptive Morphological Closing and Local Adaptive Thresholding.” 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), no. Ipria: 146–50. https://doi.org/10.1109/PRIA.2017.7983035.

Haryoko, Andy, and Sholeh Hadi Pramono. 2016. “Pengenalan Karakter Plat Kendaraan Bermotor Berbasis Citra Dengan Menggunakan Metode Canny Dan Algoritma Backpropagation” 1 (2): 93–105.

Hidayah, Maulidia R, Isa Akhlis, and Endang Sugiharti. 2017. “Recognition Number of The Vehicle Plate Using Otsu Method and K-Nearest Neighbour Classification” 4 (1): 66–75.

Ikhsanuddin, Rohmatulloh Muhamad. 2014. “Identifikasi Citra Pada Plat Nomor Kendaraan Mobil Pribadi Menggunakan Metode K-Nearest Neighbour,” 1–7.

Komarudin, Abdillah, Ahmad Teguh Satria, and Wiedjaja Atmadja. 2015. “Designing License Plate Identification through Digital Images with OpenCV.” Procedia - Procedia Computer Science 59 (Iccsci): 468–72. https://doi.org/10.1016/j.procs.2015.07.517.

Lahmurahma, Hafara Fisca. 2013. Perbandingan Dalam Pengenalan Karakter Plat Nomor Kendaraan Menggunakan Image Centroid And Zone Dengan Klasifikasi K-Nearest Neighbour Dan Probabilistic Neural Network. IPB(institut Pertanian Bogor. https://repository.ipb.ac.id/handle/123456789/67786.

Li, Hui, Peng Wang, Mingyu You, and Chunhua Shen. 2018. “Reading Car License Plates Using Deep Neural Networks ଝ.” Image and Vision Computing 72: 14–23. https://doi.org/10.1016/j.imavis.2018.02.002.

Logitect, hd-webcam-C310, (copyright 2019), Retrieved from https://www.logitech.com/id-id/product/hd-webcam-c310

Megalingam, Rajesh Kannan, Prasanth Krishna, Vishnu A Pillai, and Reswan V I Hakkim. 2010. “Extraction of License Plate Region in Automatic License Plate Recognition.” 2010 International Conference on Mechanical and Electrical Technology 640 (Icmet): 496–501. https://doi.org/10.1109/ICMET.2010.5598409.

OpenCV team, (copyright 2019), Retrieved from https://opencv.org/

Python, (copyright 2001-2019), Retrieved from https://www.python.org/

Raspberry pi foundation , raspberry-pi-2-model-b, (2019, June 20), Retrieved from https://www.raspberrypi.org/products/raspberry-pi-2-model-b/

Saleem, Nauman. 2016. “Automatic License Plate Recognition Using Extracted Features.” 2016 4th International Symposium on Computational and Business Intelligence (ISCBI), 221–25. https://doi.org/10.1109/ISCBI.2016.7743288.

Sitompul, Andre, Mahmud Dwi Sulistiyo, and Bedy Purnama. 2016. “Indonesian Vehicles Number Plates Recognition System Using Multi Layer Perceptron Neural Network And” 1 (December): 29–37.

Unnikrishnan, Arya P, Roshini Romeo, and Fabeela Ali Rawther. 2016. “License Plate Localization Using Genetic Algorithm Including Color Feature Extraction.” Procedia Technology 24: 1445–51. https://doi.org/10.1016/j.protcy.2016.05.173.

Yogheedha, K. 2018. “Automatic Vehicle License Plate Recognition System Based on Image Processing and Template Matching Approach.” 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), 1–8.


Article Metrics

 Abstract Views : 1536 times
 PDF Downloaded : 434 times

Refbacks

  • There are currently no refbacks.