|Table of Contents|

Small Sample License Plate Recognition Based on Sample Expansion(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年03期
Page:
1-10
Research Field:
·全国机器学习会议论文专栏·
Publishing date:

Info

Title:
Small Sample License Plate Recognition Based on Sample Expansion
Author(s):
You Mingyu; Han Xuan
College of Electronics and Information Engineering,Tongji University,Shanghai 201804,China
Keywords:
license plate recognition with small dataset; generative adversarial network; convolutional neural network; bidirectional recurrent neural network
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.03.001
Abstract:
As one of the significant topics of intelligent transportation system,license plate recognition technology has been widely studied. In recent years,the rapid development of deep learning has provided a good solution for it. However,in actual situations,it is difficult for researchers to collect enough data to support the training of the model sometimes. This paper focuses on the license plate recognition with small amount of data,and proposes a method to generate training data with adversarial generation network to assist the recognition model training. First,a large-scale image set is generated using the generator of GAN. Then,these images are fed to a deep convolutional neural network followed by a bidirectional recurrent neural network with long short-term memory,which performs the feature learning and sequence labelling. Finally,the pre-trained model is fine-tuned on real images. Our experimental results on a few data sets demonstrate the effectiveness of using GAN images:an improvement of 7.5 recognition accuracy percent points over a strong baseline with moderate-sized real data being available. The proposed framework achieves competitive recognition accuracy on challenging test datasets. This paper also leverages the depthwise separate convolution to construct a lightweight convolutional recurrent neural network,which is about half size and 2×faster on CPU. Combining this framework and the proposed pipeline,this paper makes progress in performing accurate recognition on mobile and embedded devices.

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Last Update: 2019-09-30