Study for License Plate Detection

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Author(s)

Mie Mie Aung 1,* Phyu Phyu Khaing 2 Myint San 1

1. University of Computer Studies (Monywa), Myanmar

2. Myanmar Institute of Information Technology, Mandalay, Myanmar

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2019.12.05

Received: 15 Sep. 2019 / Revised: 3 Oct. 2019 / Accepted: 24 Oct. 2019 / Published: 8 Dec. 2019

Index Terms

License plate detection, image processing, edge detection algorithm, morphological operations, adaptive thresholding algorithm

Abstract

License Plate Detection (LPD) system is the application of computer vision and image processing technology. LPD system is the first and main step of License Plate Recognition (LPR) system. So, it performs as the main driver of the LPR system. License plate detection step is always performed in front of the license plate recognition step. LPD system takes the vehicle images as input, follows with the general steps: such as reprocessing, localization, region extraction, and region detection, and the detected image are the output of the system. There are many algorithms for LPD while detecting a license plate in different conditions is still a complex task. For the LPD system, morphological operation and deep learning model are mostly used. This paper presents the critical study of the license plate detection system and also examines the implementation of new technologies of the license plate detection system.

Cite This Paper

Mie Mie Aung, Phyu Phyu Khaing, Myint San, " Study for License Plate Detection", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.12, pp. 39-46, 2019. DOI: 10.5815/ijigsp.2019.12.05

Reference

[1]Shi J, Malik J., “Normalized Cuts and Image Segmentation”, IEEE Computer Society, 2000.

[2]Felzenszwalb P F, Huttenlocher D P., “Efficient Graph-Based Image Segmentation”, International Journal of Computer Vision, 2004, 59(2):167-181.

[3]Pal N R., “A review on image segmentation techniques”, Pattern Recognit, 1993, 26(9):1277-1294.

[4]Boykov Y., “Interactive graph cuts for optimal boundary and region segmentation of object in N-D image segmentation”, In Proceedings eighth IEEE international conference on computer vision., ICCV 2001, Vol. 1, pp. 105-112.

[5]Z. K. Huang, and L. Y. Hou, “Chinese License Plate Detection Based on Deep Neural Network”, International Conference on Control and Robots (ICCR)(pp. 84-88), 2018.

[6]Mao, Shangqin, X. Huang, and M. Wang., “An adaptive method for Chinese license plate location”, Intelligent Control and Automation IEEE, 2010:6173-6177.

[7]G. Rabbani, M.A. Islam, M.A. Azim, M.K. Islam, and Md.M. Rahman, “Bangladeshi License Plate Detection and Recognition with Morphological Operation and Convolution Neural Network”, 2018 21st International Conference of Computer and Information Technology (ICCIT), pp. 1-5. IEEE, 2018.  

[8]Y. Yuan, W.Zou, Y. Zhao, X. Wang X. Hu, and N. Komodakis, “A Robust and Efficient Approach to License Plate Detection”, IEEE Transactions on Image Processing, 26(3), pp. 1102-1114, 2017.

[9]Chao Gou, Kunfeng Wang, Yanjie Yao, and Zhengxi Li, “Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines”, IEEE Transactions on Intelligent Transportation Systems, 17(4), pp.1096-1107, 2015.

[10]Davis, A. M., Arunvinodh, C., and Np, A. M. , “Automatic license plate detection using vertical edge detection method.”, In 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-6), 2015, March, IEEE. 

[11]Babak Abad Fomani, and Asadollah Shahbahrami, “License plate detection using adaptive morphological closing and local adaptive thresholding”, 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), pp. 146-150, 2017.

[12]H. Li, R. Yang, and X. Chen, “License plate detection using convolutional neural network”, 3rd  International Conference on Computer and Communication, pp. 1736-1740,2017.

[13]Y. Shi, and Y. Chen, “License plate detection based on convolutional neural network and visual feature”, International Conference on Mechanical, Control and Computer Engineering (ICMCCE), pp. 514-519, 2018.

[14]D. Bradley and G. Roth, “Adaptive thresholding using the integral image,” J. Graph., GPU, Game Tools, vol. 12, no. 2, pp. 13–21, 2007.