文章基本信息

标题：License Plates Detection and Recognition with Multi-Exposure Images
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作者：Seong-O Shim ; Romil Imtiaz ; Asif Siddiq 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：4
DOI：10.14569/IJACSA.2022.0130435
语种：English
出版社：Science and Information Society (SAI)
摘要：Automatic License Plate Recognition (ALPR) has been an important research topic for many years in the intelligent transportation system and image recognition fields. License Plate (LP) detection and recognition has always been a challenging issue due to several factors, including different weather and lighting, unavoidable data acquisition noise, and requirement for real-time performance in state-of-the-art Intelligent Transportation Systems (ITS) applications. Different techniques have been proposed based on machine learning, deep learning, and image processing for the detection and recognition of LPs. This paper proposes a method that performs vehicle LP detection and character recognition with high accuracy by using artificially generated multi-exposure images of the LP. First, one under-exposed and three over-exposed images are generated from a reference image taken from the camera. Then, LP detection and character recognition algorithms are applied on these five images – one real image and four synthesized images. At each character location in LP, the character detected with the highest confidence level among these images is selected as the final predicted character. The system is fully automated, and no pre-processing, calibration, or configuration procedures are needed. Experimental results show that the proposed method achieves high accuracy and works robustly even in challenging conditions. The proposed method can be used in any existing ALPR system and the results on three recent ALPR techniques show that the accuracies are further improved when they are combined with the proposed method.
关键词：License plate detection; character recognition; multi-exposure images; convolutional neural networks