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A vehicle plate recognition system based on deep learning algorithms

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Abstract

In modern life, the massive number of vehicles makes it hard for a human being to process its related information. So, it is important to build an automatic system to collect information about vehicles. The license plate is the unique identifier of a vehicle. In this paper, we propose an automatic license plate recognition system. The proposed system was based on the Faster R-CNN improved by adding an adaptive attention network for the segmentation of the license plate to retrieve the numbers and the letters of identification. Also, we add a deconvolution layer at the top of the features extraction network to detect the small size of the target license plate. To train and evaluate the proposed system, a dataset was collected for Arabic countries such as Egypt, KSA, and UAE that have similar license plates with Arabic and Indian numbers, Arabic and Latin alphabets. The dataset was collected from the internet using a python script then it was filtered and annotated manually. The evaluation of the proposed model dataset results in achieving a recall of 98.65 % and a precision of 97.46 %. The developed system was able to process images in real-time with a processing speed of 23 FPS.

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Abbreviations

faster R-CNN:

Faster region based convolutional neural network

RPN:

Region proposal network

ROI:

Region of interest

IoU:

Internet of Thing

AAN:

Adaptive attention network

yolo:

You look only once

GPU:

Graphics processing units

CPU:

Central processing units

SSD:

Single Shot Multi-Box Detector

RFCN:

Region-based Fully Convolutional Networks

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Acknowledgements

The authors wish to acknowledge the approval and the support of this research study by the grant N_. CIT-2018-3-9-F-7617 from the Deanship of the Scientific Research in Northern Border University, Arar, KSA.

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Authors and Affiliations

  1. Department of Computer Sciences, Faculty of Computing & Information Technology, Northern Border University, Rafha, Saudi Arabia

    Taoufik Saidani & Yamen El Touati

  2. Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia

    Taoufik Saidani

  3. Oasis Laboratory, National Engineering School, El Manar University, Tunis, Tunisia

    Yamen El Touati

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  1. Taoufik Saidani
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  2. Yamen El Touati
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Correspondence to Taoufik Saidani.

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Saidani, T., Touati, Y.E. A vehicle plate recognition system based on deep learning algorithms. Multimed Tools Appl 80, 36237–36248 (2021). https://doi.org/10.1007/s11042-021-11233-z

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  • DOI: https://doi.org/10.1007/s11042-021-11233-z

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