Research on License Plate Recognition Algorithms Based on Deep Learning in Complex Environment
License plate recognition systems are widely used in modern smart cities, such as toll payment systems, parking fee payment systems and residential access control. Such electronic systems are not only convenient for people’s daily life, but also provide safe and efficient services for man...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9092977/ |
| _version_ | 1811309156453318656 |
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| author | Wang Weihong Tu Jiaoyang |
| author_facet | Wang Weihong Tu Jiaoyang |
| author_sort | Wang Weihong |
| collection | DOAJ |
| description | License plate recognition systems are widely used in modern smart cities, such as toll payment systems, parking fee payment systems and residential access control. Such electronic systems are not only convenient for people’s daily life, but also provide safe and efficient services for managers. License plate recognition algorithm is a mature but imperfect technology. The traditional location recognition algorithm is easily affected by light, shadow, background complexity or other factors, resulting in the failure to meet the application of real scenes. With the development of deep learning, the license plate recognition algorithm can extract deeper features, thus greatly improving the detection and recognition accuracy. Therefore, this paper discusses the application of deep learning in license plate recognition, and the main work is as follows: 1) Introduce the most advanced algorithms from the three main technical difficulties: license plate skew, image noise and license plate blur; 2) According to the process, the deep learning algorithms are classified into direct detection algorithms and indirect detection algorithms, and the advantages and disadvantages of the current license plate detection algorithms and character recognition algorithms are analyzed; 3) The differences in data sets, workstation, accuracy and time of different license plate recognition systems are compared; 4) Compare and illustrate the existing public license plate datasets according to the number of pictures, resolution and environmental complexity, and make a prospect for the future research direction of license plate recognition. |
| first_indexed | 2024-04-13T09:37:13Z |
| format | Article |
| id | doaj.art-f09f5a1bb3f946c5a9ba46a763cecb86 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T09:37:13Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f09f5a1bb3f946c5a9ba46a763cecb862022-12-22T02:52:05ZengIEEEIEEE Access2169-35362020-01-018916619167510.1109/ACCESS.2020.29942879092977Research on License Plate Recognition Algorithms Based on Deep Learning in Complex EnvironmentWang Weihong0Tu Jiaoyang1https://orcid.org/0000-0002-3229-0490Department of Computing, Zhejiang University of Technology, Hangzhou, ChinaSchool of Computing, Zhejiang University of Technology, Hangzhou, ChinaLicense plate recognition systems are widely used in modern smart cities, such as toll payment systems, parking fee payment systems and residential access control. Such electronic systems are not only convenient for people’s daily life, but also provide safe and efficient services for managers. License plate recognition algorithm is a mature but imperfect technology. The traditional location recognition algorithm is easily affected by light, shadow, background complexity or other factors, resulting in the failure to meet the application of real scenes. With the development of deep learning, the license plate recognition algorithm can extract deeper features, thus greatly improving the detection and recognition accuracy. Therefore, this paper discusses the application of deep learning in license plate recognition, and the main work is as follows: 1) Introduce the most advanced algorithms from the three main technical difficulties: license plate skew, image noise and license plate blur; 2) According to the process, the deep learning algorithms are classified into direct detection algorithms and indirect detection algorithms, and the advantages and disadvantages of the current license plate detection algorithms and character recognition algorithms are analyzed; 3) The differences in data sets, workstation, accuracy and time of different license plate recognition systems are compared; 4) Compare and illustrate the existing public license plate datasets according to the number of pictures, resolution and environmental complexity, and make a prospect for the future research direction of license plate recognition.https://ieeexplore.ieee.org/document/9092977/Image processingimage analysisimage classificationlicense plate recognitiondeep learning |
| spellingShingle | Wang Weihong Tu Jiaoyang Research on License Plate Recognition Algorithms Based on Deep Learning in Complex Environment IEEE Access Image processing image analysis image classification license plate recognition deep learning |
| title | Research on License Plate Recognition Algorithms Based on Deep Learning in Complex Environment |
| title_full | Research on License Plate Recognition Algorithms Based on Deep Learning in Complex Environment |
| title_fullStr | Research on License Plate Recognition Algorithms Based on Deep Learning in Complex Environment |
| title_full_unstemmed | Research on License Plate Recognition Algorithms Based on Deep Learning in Complex Environment |
| title_short | Research on License Plate Recognition Algorithms Based on Deep Learning in Complex Environment |
| title_sort | research on license plate recognition algorithms based on deep learning in complex environment |
| topic | Image processing image analysis image classification license plate recognition deep learning |
| url | https://ieeexplore.ieee.org/document/9092977/ |
| work_keys_str_mv | AT wangweihong researchonlicenseplaterecognitionalgorithmsbasedondeeplearningincomplexenvironment AT tujiaoyang researchonlicenseplaterecognitionalgorithmsbasedondeeplearningincomplexenvironment |