An efficient texture descriptor for the detection of license plates from vehicle images in difficult conditions

Al-Shemarry, Meeras Salman ORCID: https://orcid.org/0000-0003-2859-9441 and Li, Yan and Abdulla, Shahab ORCID: https://orcid.org/0000-0002-1193-6969 (2020) An efficient texture descriptor for the detection of license plates from vehicle images in difficult conditions. IEEE Transactions on Intelligent Transportation Systems, 21 (2). pp. 553-564. ISSN 1524-9050


Abstract

This paper aims to identify the license plates under difficult image conditions, such as low/high contrast, foggy, distorted, and dusty conditions. This paper proposes an efficient descriptor, multi-level extended local binary pattern, for the license plates (LPs) detection system. A pre-processing Gaussian filter with contrast-limited adaptive histogram equalization enhancement method is applied with the proposed descriptor to capture all the representative features. The corresponding bins histogram features for a license plate image at each different level are calculated. The extracted features are used as the input to an extreme learning machine classifier for multiclass vehicle LPs identification. The dataset with English cars LPs is extended using an online photo editor to make changes on the original dataset to improve the accuracy of the LPs detection system. The experimental results show that the proposed method has a high detection accuracy with an extremely high computational efficiency in both training and detection processes compared to the most popular detection methods. The detection rate is 99.10% with a false positive rate of 5% under difficult images. The average training and detection time per vehicle image is 4.25 and 0.735 s, respectively.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Open Access College (1 Jul 2013 - 7 Jun 2020)
Faculty/School / Institute/Centre: Historic - Open Access College (1 Jul 2013 - 7 Jun 2020)
Date Deposited: 07 May 2019 05:13
Last Modified: 20 Apr 2021 05:39
Uncontrolled Keywords: extreme learning machine, local binary pattern, extended local binary pattern, license plate detection
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: https://doi.org/10.1109/TITS.2019.2897990
URI: http://eprints.usq.edu.au/id/eprint/36040

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