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Car license plate detection method for Malaysian plates-styles by using a web camera


Citation

Al-Ghaili, Abbas Mohammed Ali and Mashohor, Syamsiah and Ramli, Abdul Rahman and Ismail, Alyani (2010) Car license plate detection method for Malaysian plates-styles by using a web camera. Pertanika Journal of Science & Technology, 18 (2). pp. 303-319. ISSN 0128-7680; ESSN: 2231-8526

Abstract / Synopsis

Recently, license plate detection has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them work under restricted conditions such as fixed illumination, stationary background, and high resolution images. License plate detection plays an important role in car license plate recognition systems because it affects the accuracy and processing time of the system. This work aims to build a Car License Plate Detection (CLPD) system at a lower cost of its hardware devices and with less complexity of algorithms' design, and then compare its performance with the local CAR Plate Extraction Technology (CARPET). As Malaysian plates have special design and they differ from other international plates, this work tries to compare two likely-design methods. The images are taken using a web camera for both the systems. One of the most important contributions in this paper is that the proposed CLPD method uses Vertical Edge Detection Algorithm (VEDA) to extract the vertical edges of plates. The proposed CLPD method can work to detect the region of car license plates. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 out of 643 images were successfully detected. Meanwhile, the average accuracy of locating car license plate was 90%. In this work, a comparison between CARPET and the proposed CLPD method for the same tested images was done in terms of detection rate and efficiency. The results indicated that the detection rate was 92% and 84% for the CLPD method and CARPET, respectively. The results also showed that the CLPD method could work using dark images to detect license plates, whereas CARPET had failed to do so.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
Publisher: Universiti Putra Malaysia Press
Keywords: Adaptive thresholding; Car License Plate Detection (CLPD); CAR Plate Extraction Technology (CARPET); Vertical Edge Detection Algorithm (VEDA)
Depositing User: Noor Syafini Zamani
Date Deposited: 23 Nov 2015 08:50
Last Modified: 14 Jul 2016 10:44
URI: http://psasir.upm.edu.my/id/eprint/40579
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