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http://dx.doi.org/10.15207/JKCS.2021.12.1.029

Detection of Number and Character Area of License Plate Using Deep Learning and Semantic Image Segmentation  

Lee, Jeong-Hwan (Department of Electronic Engineering, Andong University)
Publication Information
Journal of the Korea Convergence Society / v.12, no.1, 2021 , pp. 29-35 More about this Journal
Abstract
License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.
Keywords
Deep Learning; Convolution Neural Network(CNN); Semantic Segmentation; License Plate; Image Segmentation and Recognition;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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