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License Plate Recognition using Capsule Networks with an Improved Dynamic Routing Algorithm

Title
License Plate Recognition using Capsule Networks with an Improved Dynamic Routing Algorithm
Author
HUANGRUI
Advisor(s)
조인휘
Issue Date
2019-02
Publisher
한양대학교
Degree
Master
Abstract
License Plate Recognition is a practical application based on computer vision. With the development of artificial intelligence and autonomous driving technology, this application plays a very big role in the system development. A traditional method is used by the convolutional neural network(CNN) for identification. But due to the intrinsic inability of max pooling layer, Convolutional Neural Network(CNN) fail to capture pose, view and orientation of the images. CNNs typically require a large amount of training data and cannot handle input process correctly. The Capsule Network is a recently introduced new machine learning architecture designed to overcome these disadvantages of CNN and contribute to a radical change in deep learning solutions. Because of robust to rotational and affine transformations of images, the capsule network is well suited to the processing of license plate image data sets. In this paper, to improve the accuracy of license plate recognition, we adopt a novel method for this task using deep learning architecture called capsule networks. The capsule network consists of capsules, which are a set of neurons that use dynamic routing algorithm to represent object instantiation parameters, such as pose and orientation. First, in order to solve the problem that the license plate picture is difficult to collect, we designed a license plate image generation system to generate the data set. Second, we use the capsule network to implement the license plate recognition system, and design CNN as an experimental control group. Finally, we tested on the real license plate dataset. The result shows that the Capsule network’s accuracy rate on the test data set reaches 91.3%, and it is improved by 2.34% compared with the traditional convolutional neural network. In addition, we also designed a new capsule network improved dynamic routing algorithm for solving the problem that the capsule network training time is too long. And we also compare the performance of CNN and capsule network under the same training data set. Our results show that the training speed is increased by 31.9%.
URI
https://repository.hanyang.ac.kr/handle/20.500.11754/99784http://hanyang.dcollection.net/common/orgView/200000434598
Appears in Collections:
GRADUATE SCHOOL[S](대학원) > COMPUTER SCIENCE(컴퓨터·소프트웨어학과) > Theses (Master)
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