10.57647/j.spre.2024.0804.23

Car license plate detection by color processing and edge detection methods

  1. Department of Electrical Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran
  2. Department of Electrical Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran  AND  Saman Industrial Complex, Tehran, Iran
  3. Saman Industrial Complex, Tehran, Iran
Car license plate detection by color processing and edge detection methods

Received: 2024-10-15

Revised: 2024-11-07

Accepted: 2024-11-10

Published in Issue 2025-01-16

How to Cite

Samadi, A. M., Ghabeli, L., & Sabbaghi Nadooshan, R. (2025). Car license plate detection by color processing and edge detection methods. Signal Processing and Renewable Energy (SPRE), 8(4), 1-9. https://doi.org/10.57647/j.spre.2024.0804.23

PDF views: 222

Abstract

One of the main processes in intelligent transportation systems is car license plate detection. The most important part of this  algorithm is detecting the blue part on the left side of license plates. After identifying the blue part (width) of a license plate, the  length of the license plate can be determined since its width is proportional to its length. A new algorithm is presented in this paper to simultaneously perform the thresholding operation on the color and edge of the images obtained after proper preprocessing. Since the blue part of a license plate is near its left edge, the common features of the two obtained images are merged. The merged image is then used in Hough transform to detect the license plate. After detecting the license plate and according to the constant ratio of the width of each character to the length of the license plate, the location of each character is determined and the value of the character is recognized using a multilayer perceptron (MLP) neural network. Since this is a cascaded method, it can benefit from the advantages of other methods in different areas, such as color processing, edge detection, and morphological operations. Moreover, because most images are color images, the proposed method effectively uses color features, while this usage is less  common in the literature. To evaluate the accuracy of the proposed method, some images are collected using a mobile phone camera or randomly from the Internet. A total of 288 images are used to assess the accuracy of the license plate detection, and 2200 images are used to measure the validity of the license plate character recognition. The accuracy of detecting license plates is 93.75% in this study. 

Keywords

  • Car license plate detectio,
  • Color processin,
  • Edge processing

References

  1. D. Wang, Y. Tian, W. Geng, L. Zhao, and C. J. P. R. L. Gong, "LPR-Net: Recognizing Chinese license plate in complex environments," Pattern Recognition Letters, vol. 130, pp. 148-156, 2020.
  2. A. Rio-Alvarez, J. de Andres-Suarez, M. Gonzalez-Rodriguez, D. Fernandez-Lanvin, and B. J. S. P. López Pérez, "Effects of challenging weather and illumination on learning-based license plate detection in noncontrolled environments," Scientific Programming, vol. 2019, pp. 16-1, 2019.
  3. K. Raghunandan et al., "Riesz fractional based model for enhancing license plate detection and recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 9, pp. 2276-2288, 2018.
  4. S. Azam, M. M. J. J. o. V. C. Islam, and I. Representation, "Automatic license plate detection in hazardous condition," Journal of Visual Communication and Image Representation, vol. 36, pp. 172-186, 2016.
  5. Y.-T. Chen, J.-H. Chuang, W.-C. Teng, H.-H. Lin, and H.-T. Chen, "Robust license plate detection in nighttime scenes using multiple intensity IR-illuminator," in 2012 IEEE International Symposium on Industrial Electronics, 2012, pp. 893-898: IEEE.
  6. Y. Wen, Y. Lu, J. Yan, Z. Zhou, K. M. von Deneen, and P. J. I. T. o. i. t. s. Shi, "An algorithm for license plate recognition applied to intelligent transportation system," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 830-845, 2011.
  7. M. S. Al-Shemarry and Y. J. I. A. Li, "Developing Learning-Based Preprocessing Methods for Detecting Complicated Vehicle Licence Plates," vol. 8, pp. 170951-170966, 2020.
  8. E. E. Etomi, D. U. J. T. J. o. S. Onyishi, and Technology, "Automated number plate recognition system," vol. 2, no. 1, pp. 38-48, 2021.
  9. X. Yan, C. Wang, D. Hao, and M. Chen, "License Plate Detection Using Bayesian Method Based on Edge Features," in 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP), 2021, pp. 205-211: IEEE.
  10. A. Tourani, A. Shahbahrami, S. Soroori, S. Khazaee, and C. Y. J. I. A. Suen, "A robust deep learning approach for automatic iranian vehicle license plate detection and recognition for surveillance systems," vol. 8, pp. 201317-201330, 2020.
  11. S. Xiao, W. Yang, B. Cao, H. Zhou, and C. He, "An Efficient Methodology for License Plate Localization and Recognition with Low Quality Images," in Journal of Physics: Conference Series, 2021, vol. 1757, no. 1, p. 012084: IOP Publishing.
  12. V. Abolghasemi, A. J. I. Ahmadyfard, and V. Computing, "An edge-based color-aided method for license plate detection," Image and Vision Computing, vol. 27, no. 8, pp. 1134-1142, 2009.
  13. Z. Baohua, Y. Dahua, H. Hongmei, and G. Lanying, "License plate location algorithm based on histogram equalization," in 2010 International Conference On Computer Design and Applications, 2010, vol. 1, pp. V1-517-V1-519: IEEE.
  14. A. M. Al-Ghaili, S. Mashohor, A. R. Ramli, and A. J. I. t. o. v. t. Ismail, "Vertical-edge-based car-license-plate detection method," IEEE Transactions on Vehicular Technology, vol. 62, no. 1, pp. 26-38, 2012.
  15. S. Yu, B. Li, Q. Zhang, C. Liu, and M. Q.-H. J. P. R. Meng, "A novel license plate location method based on wavelet transform and EMD analysis," Pattern Recognition, vol. 48, no. 1, pp. 114-125, 2015.
  16. V. Tadic, M. Popovic, and P. J. E. A. o.O. A. I. Odry, "Fuzzified Gabor filter for license plate detection," Engineering Applications of Artificial Intelligence, vol. 48, pp. 40-58, 2016.
  17. A. N. Vicente and H. Pedrini, "A learning-based single-image super-resolution method for very low quality license plate images," in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016, pp. 515-520.
  18. R. Panahi and I. J. I. T. o. i. t. s. Gholampour, "Accurate detection and recognition of dirty vehicle plate numbers for high-speed applications," IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 18, no. 4, pp. 767-779, 2016.
  19. M. K. Saini, S. J. J. o. V. C. Saini, and I. Representation, "Multiwavelet transform based license plate detection," Journal of Visual Communication and Image Representation, vol. 44, pp. 128-138, 2017.
  20. Y. Yuan, W. Zou, Y. Zhao, X. Wang, X. Hu, and N. J. I. T. oO. I. P. Komodakis, "A robust and efficient approach to license plate detection," IEEE Transactions on Image Processing, vol. 26, no. 3, pp. 1102-1114, 2016.
  21. J. J. I. T. o. p. a. Canny and m. intelligence, "A computational approach to edge detection," IEEE Transactions on pattern analysis and machine intelligence, no. 6, pp. 679-698, 1986.
  22. N. Omar, A. Sengur, and S. G. S. J. E. S. w. a. Al-Ali, "Cascaded deep learning-based efficient approach for license plate detection and recognition," Expert Systems With Applications, vol. 149, p. 113280, 2020.
  23. M. R. Asif, C. Qi, T. Wang, M. S. Fareed, S. A. J. C. Raza, and E. Engineering, "License plate detection for multi-national vehicles: An illumination invariant approach in multi-lane environment," Computers and Electrical Engineering, vol. 78, pp. 132-147, 2019.
  24. J. J. S. Kim, "Automatic vehicle license plate extraction using region-based convolutional neural networks and morphological operations," vol. 11, no. 7, p. 882, 2019.
  25. P. Pramkeaw, M. Ketcham, W. Limpornchitwilai, and N. Chumuang, "Analysis of detecting and interpreting warning signs for distance of cars using analyzing the license plate," in 2019 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), 2019, pp. 1-8: IEEE.
  26. M. A. Hussein and A. H. J. A.-M. J. o. S. Abbas, "Plant Leaf Disease Detection Using Support Vector Machine," Al-Mustansiriyah Journal of Science, vol. 30, no. 1, pp. 105-110, 2019.