Iraqi license plate recognition system using (YOLO) with SIFT and SURF Algorithm

Journal > Journal > Journal Vol – 15 No -8, August 2020 > Iraqi license plate recognition system using (YOLO) with SIFT and SURF Algorithm

August 22, 2020

Journal Vol – 15 No -8, August 2020

Authors:

Nada Hassan Jasem,Faisal Ghazi. Mohammed,

DOI NO:

https://doi.org/10.26782/jmcms.2020.08.00048

Keywords:

Automatic License Recognition,deep learning methods,Iraqi plates,SIFT and SURF algorithm,training phase,testing phase,

Abstract

Automatic License Recognition (ALPR) has been considered significant in many applications in intelligent transport and monitoring systems. As in other tasks of the computer vision, deep learning methods (DL) were implemented recently in the ALPR context, with a focus on country-specific Iraqi councils, like German or Old and Northern.  In this work, we proposed the DL-ALPR system from the beginning in the license plate detection phase of Iraqi plates according to the latest (YOLO) convolutional layers to detect single class. Utilizing a data set of Iraqi paintings collected by the researcher, and in the second stage, the detection plates are Recognition by extracting a set of license plate features using the SIFT and SURF algorithm, then using KNN to match the plates stored in the database to match them, the data is divided into two parts, part photos: 1300 pictures, And the second part, videos of the Iraqi vehicles in different environmental conditions, and the number is 35 videos. 1300 photos were divided 70% in the training phase and 30% in the testing phase and the results obtained in the testing phase were 99.2% for LP detection and 97.14% for recognition and the total accuracy of the system was 98.17%.

Refference:

I. A. Khazri, “Automatic License Plate Detection & Recognition using deep learning”, towards data science, 2019. web: https://towardsdatascience.com/automatic-license-plate-detection-recognition-using-deep-learning-624def07eaaf?gi=fc80f0526b7.

II. Saharkiz, “Nearest Neighbor Algorithm Implementation and Overview ” , Code project, 2009. Web: https://www.codeproject.com/Articles/32970/K-Nearest-Neighbor-Algorithm-Implementation-and-Ov.
III. D. Hoiem, Y. Chodpathumwan, Q. Dai, “Diagnosing Error in Object Detectors”, Computer Vision – ECCV, Springer Berlin Heidelberg, Berlin, Heidelberg, Vol. 1, No. 1, Pp. 340-353, 2012.
IV. H. Bay, T. Tuytelaars, L. Van Gool, “SURF: Speeded Up Robust Features”, Computer Vision – ECCV, Springer Berlin Heidelberg, Berlin, Heidelberg, Vol. 5, No. 1, Pp. 404-417, 2006.
V. H. A.-H. Kahdum, “Leukocytes Image Segmentation and Classification Based on Geometrical Features and Naïve Bayes Classifier,” Master Degree, College of Science, University of Baghdad, Baghdad – Iraq, 2019.
VI. Kusumadewi, C.A. Sari, E.H. Rachmawanto, “License Number Plate Recognition using Template Matching and Bounding Box Method”, Journal of Physics: Conference Series, IOP Publishing, Vol. 1, No. 1, Pp. 012067,2019.
VII. J. Kim, “Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations”, Symmetry, Vol. 11, No. 7, Pp. 882,2019.

VIII. J. Redmon, A. Farhadi, “YOLO9000: better, faster, stronger”, Proceedings of the IEEE conference on computer vision and pattern recognition, Vol. 1, No. 1, Pp. 7263-7271, 2017.

IX. J.T. Pedersen, “Study group SURF: Feature detection & description”, Department of Computer Science, Aarhus University, Pp. 1-12,2011

X. Mathew, Sheena S, “A Comparison of Sift And Surf Algorithm For The Recognition of An Efficient Iris Biometric System”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, No. 1, Pp. 37–42, 2016.

XI. P. Marzuki, F. Radzi, Y.C. Wong, N. Abdul Hamid, N. Ali, M. Mat ibrahim, “A design of license plate recognition system using convolutional neural network”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 9, No. 2196, Pp.2, 2019.

XII. R. Girshick, “Fast r-cnn”, Proceedings of the IEEE international conference on computer vision, Vol. 1, No. 1, Pp. 1440-1448, 2015.
XIII. S. Geethapriya, N. Duraimurugan, S. Chokkalingam, “Real-Time Object Detection with Yolo”, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 8, No. 1, Pp. 1440-1448, 2019.

XIV. S. Geethapriya, N. Duraimurugan, S. Chokkalingam, “Real-Time Object Detection with Yolo”, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 8, No. 1, Pp. 1440-1448, 2019.

View Download