基于深度学习的端到端车牌检测识别系统

被引：8

作者：

曹正凤 ^{[1
]}

李少丁 ^{[1
]}

王栋梁 ^{[1
]}

徐建波 ^{[2
]}

刘晗 ^{[2
]}

卓晴 ^{[2
]}

机构：

[1] 北京市高速公路智能交通工程技术研究中心

[2] 清华大学自动化系

来源：

中国交通信息化 | 2018年 / 09期

关键词：

深度学习; 车牌检测; 车牌识别; 端到端; 无分割;

D O I：

10.13439/j.cnki.itsc.2018.09.008

中图分类号：

TP391.41 []; TP181 [自动推理、机器学习];

学科分类号：

080203 ; 081104 ; 0812 ; 0835 ; 1405 ;

摘要：

针对传统的车牌检测和识别任务,本文提出一个在自然场景下完成端到端的车牌检测和识别任务的深度学习模型,利用该模型,可以同时完成车牌的检测和无分割识别任务。整个深度学习模型可以进行端到端训练。对比现有的车牌检测和识别算法,本文提出的深度学习模型,通过一个神经网络模型,可以联合解决车牌的检测和识别这两个子任务,通过端到端的学习,不仅可以避免误差的累积,而且可以进一步加速神经网络的收敛,提高神经网络的泛化性。在中文的车辆图片数据集中,本文提出的端到端的深度学习模型的车牌检测和无分割识别准确率达到95.4%。

引用

页码：88 / 91

页数：4

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