Multi-objective optimization based multi-task learning for end-to-end license plates recognition

被引：0
|
作者：
Zhou X.-J. [1 ,2 ]
Gao Y. [1 ]
Li C.-J. [1 ]
Yang C.-H. [1 ]
机构：
[1] School of Automation, Central South University, Changsha
[2] Hunan Xiangjiang Artificial Intelligence Academy, Changsha
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2021年 / 38卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Deep neural network; License plate recognition; Machine learning; Multi-objective optimization; Multi-task learning;
D O I：
10.7641/CTA.2020.00460
中图分类号：
学科分类号：
摘要：
In view of the competition and conflict among multiple license plate recognition tasks and the difficulty to improve the recognition rate of multiple license plates at the same time, a multi-objective optimization based multi-task learning for end-to-end car license plates recognition is studied in this paper. Firstly, by analyzing the difficulties that some license plate recognition tasks tend to dominate while other tasks cannot be fully optimized, a license plate recognition model based on multi-task learning is built. Then, aiming at the problem of low accuracy and poor robustness caused by character segmentation, an end-to-end license plate recognition method is put forward based on multi-task learning. Finally, a multi-task learning method based on multi-objective optimization is proposed to improve the accuracy of multiple license plate recognition. The proposed method is tested on the standard license plate datasets. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can improve the accuracy, speed and robustness of license plate recognition compared with other representative methods. © 2021, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：676 / 688
页数：12
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