A COMPARISON PRE-TRAINED MODELS FOR AUTOMATIC INDONESIAN LICENSE PLATE RECOGNITION

Authors

  • Sahid Bismantoko Agency for the Assessment and Application of Technology https://orcid.org/0000-0003-4524-8088
  • M. Rosyidi Agency for the Assessment and Application of Technology
  • Umi Chasanah Agency for the Assessment and Application of Technology
  • Asep Haryono Agency for the Assessment and Application of Technology
  • Tri Widodo Agency for the Assessment and Application of Technology

Abstract

Automatic License Plate Recognition is related to the Intelligent Transportation System (ITS) that supports the road's e-law enforcement system. In the case of the Indonesian license plate, with various colour rules for font and background, and sometimes vehicle owners modify their license plate font format, this is a challenge in the image processing approach. This research utilizes pre-trained of AlexNet, VGGNet, and ResNet to determine the optimum model of Indonesian character license plate recognition. Three pre-trained approaches in CNN-based detection for reducing time for a build if model from scratch. The experiment shows that using the pre-trained ResNet model gives a better result than another two approaches. The optimum results were obtained at epoch 50 with an accuracy of 99.9% and computation time of 26 minutes. This experiment results fulfil the goal of this research.

Keywords : ALPR; ITS; CNN; AlexNet; VGGNet; ResNet

Author Biography

Sahid Bismantoko, Agency for the Assessment and Application of Technology

PTSPT BPPT

References

S. Rajaraman et al., "Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images," PeerJ, 2018.

A. T. Sasongko and M. Ivan Fanany, "Indonesia Toll Road Vehicle Classification Using Transfer Learning with Pre-Trained Resnet Models," 2019 2nd Int. Semin. Res. Inf. Technol. Intell. Syst. ISRITI 2019, pp. 373–378, 2019.

A. Abubakar, M. Ajuji, and I. U. Yahya, "Comparison of deep transfer learning techniques in human skin burns discrimination," Appl. Syst. Innov., vol. 3, no. 2, pp. 1–15, 2020.

T. E. Liang, U. U. Sheikh, and M. N. H. Mohd, "Malaysian car plate localization using region-based convolutional neural network," Bull. Electr. Eng. Informatics, vol. 9, no. 1, pp. 411–419, 2020.

H. Li, P. Wang, and C. Shen, "Toward End-to-End Car License Plate Detection and Recognition with Deep Neural Networks," IEEE Trans. Intell. Transp. Syst., vol. 20, no. 3, pp. 1126–1136, 2019.

A. Brodzicki, J. Jaworek-Korjakowska, P. Kleczek, M. Garland, and M. Bogyo, "Pre-trained deep convolutional neural network for clostridioides difficile bacteria cytotoxicity classification based on fluorescence images," Sensors (Switzerland), 2020.

M. Talo, "Convolutional neural networks for multi-class histopathology image classification," arXiv, 2019.

L. Jing and Y. Tian, "Self-supervised visual feature learning with deep neural networks: A survey," arXiv, pp. 1–24, 2019.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems, 2012.

K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016.

Downloads

Published

2021-04-28