Identifying License Plates in Distorted Vehicle Images: Detecting Distorted Vehicle Licence Plates Using a Novel Preprocessing Methods With Hybrid Feature Descriptors
Article
Al-Shemarry, Meeras Salman, Li, Yan and Abdulla, Shahab. 2023. "Identifying License Plates in Distorted Vehicle Images: Detecting Distorted Vehicle Licence Plates Using a Novel Preprocessing Methods With Hybrid Feature Descriptors." IEEE Intelligent Transportation Systems Magazine. 15 (2), pp. 6-25. https://doi.org/10.1109/MITS.2022.3210226
Article Title | Identifying License Plates in Distorted Vehicle Images: Detecting Distorted Vehicle Licence Plates Using a Novel Preprocessing Methods With Hybrid Feature Descriptors |
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ERA Journal ID | 212744 |
Article Category | Article |
Authors | Al-Shemarry, Meeras Salman, Li, Yan and Abdulla, Shahab |
Journal Title | IEEE Intelligent Transportation Systems Magazine |
Journal Citation | 15 (2), pp. 6-25 |
Number of Pages | 20 |
Year | 2023 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 1939-1390 |
1941-1197 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/MITS.2022.3210226 |
Web Address (URL) | https://ieeexplore.ieee.org/abstract/document/9929253 |
Abstract | Intelligent transportation systems (ITSs) play a key role in many people’s lives with different aspects. Most of the ITS applications include a system for detecting license plates (LPs) in transportation vehicles. A new framework for such detection systems is introduced in this article. It includes a novel technique for preprocessing, extraction, and detection stages to identify LPs from distorted vehicle images. An efficient preprocessing method is developed in this study. An enhanced, contrast-limited adaptive histogram equalization method for filtering the unwanted LP features is proposed. At the features-extraction stage, strong hybrid features from extended local binary patterns with a median robust pattern descriptor and speeded-up robust feature descriptor are applied to extract complicated features from LP areas. Those hybrid features can enhance the useful information, and the detection system performed well under difficult scenarios. In the detection stage, the trained model by an extreme learning machine (ELM) classifier, with mean-shift algorithm, is used as a detector to decide output results. Performances of the proposed framework were compared with other classifiers using true- and false-positive rates. The system’s performance improvements by the proposed method were also compared with our previous methods and the existing detection methods in the literature. The experiments on an English car LP’s database and other language vehicle images showed that the proposed method made significant improvements to the accuracy and runtime speed for the detection system under difficult image conditions. |
Keywords | Intelligent transportation systems ; license plates; vehicles |
ANZSRC Field of Research 2020 | 400607. Signal processing |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Karbala University, Iraq |
School of Mathematics, Physics and Computing | |
UniSQ College |
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