Ensemble of adaboost cascades of 3L-LBPs classifiers for license plates detection with low quality images

Al-Shemarry, Meeras Salman and Li, Yan and Abdulla, Shahab (2018) Ensemble of adaboost cascades of 3L-LBPs classifiers for license plates detection with low quality images. Expert Systems With Applications, 92. pp. 216-235. ISSN 0957-4174


Due to the plate formats and multiform outdoor illumination conditions during the image acquisition phase, it is challenging to find effective license plate detection (LPD) method. This paper aims to develop a new detection method for identifying vehicle license plates under low quality images using image processing techniques. In this research, a robust method using a large number of AdaBoost cascades with three levels pre-processing local binary patterns classifiers (3L-LBPs) are used to detect license plates (LPs) regions. The method achieves a very high accuracy for detecting LP number from one vehicle image. The proposed method was tested and trained with the images from 630 and 400 vehicles, respectively. The images involve many difficult conditions, such as low/high contrast, dusk, dirt, fogy, and distortion problems. The experimental results demonstrate very satisfactory performance for LP detection in term of speed and accuracy, and were better than the most of the existing methods. The processing time for the whole testing LPD system was about 1.63 seconds to 2 seconds. The overall probability detection, precision, and f-measurement are 98.56%, 95.9% and 97.19%, respectively; with false positive rate 5.6%.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Available online 21 September 2017. Access to Published version in accordance with the copyright policy of the published.
Faculty / Department / School: Current - Open Access College
Date Deposited: 14 Feb 2018 02:40
Last Modified: 14 May 2018 00:50
Uncontrolled Keywords: License plate detection (LPD); region of interest (ROI); adaboost learning algorithm; cascade classifier; local binary pattern classifiers (LBP)
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1016/j.eswa.2017.09.036
URI: http://eprints.usq.edu.au/id/eprint/33164

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