Recognition of Vehicle License Plates Based on Image Processing
In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/14/6292 |
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| author | Tae-Gu Kim Byoung-Ju Yun Tae-Hun Kim Jae-Young Lee Kil-Houm Park Yoosoo Jeong Hyun Deok Kim |
| author_facet | Tae-Gu Kim Byoung-Ju Yun Tae-Hun Kim Jae-Young Lee Kil-Houm Park Yoosoo Jeong Hyun Deok Kim |
| author_sort | Tae-Gu Kim |
| collection | DOAJ |
| description | In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images. |
| first_indexed | 2024-03-10T09:47:08Z |
| format | Article |
| id | doaj.art-a00d109335014495887b39c71b98633c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:47:08Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a00d109335014495887b39c71b98633c2023-11-22T03:07:14ZengMDPI AGApplied Sciences2076-34172021-07-011114629210.3390/app11146292Recognition of Vehicle License Plates Based on Image ProcessingTae-Gu Kim0Byoung-Ju Yun1Tae-Hun Kim2Jae-Young Lee3Kil-Houm Park4Yoosoo Jeong5Hyun Deok Kim6School of Electronics Engineering, IT College, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, KoreaSchool of Electronics Engineering, IT College, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, KoreaDIPVISION, 80, Daehak-ro, Buk-gu, Daegu 41566, KoreaSchool of Electronics Engineering, IT College, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, KoreaSchool of Electronics Engineering, IT College, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, KoreaDaegu-Gyeongbuk Medical Innovation Foundation, 88, Dongnae-ro, Dong-gu, Daegu 41061, KoreaSchool of Electronics Engineering, IT College, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, KoreaIn this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images.https://www.mdpi.com/2076-3417/11/14/6292deep learninglicense plate detectionimage processingSRGANCCTV image |
| spellingShingle | Tae-Gu Kim Byoung-Ju Yun Tae-Hun Kim Jae-Young Lee Kil-Houm Park Yoosoo Jeong Hyun Deok Kim Recognition of Vehicle License Plates Based on Image Processing Applied Sciences deep learning license plate detection image processing SRGAN CCTV image |
| title | Recognition of Vehicle License Plates Based on Image Processing |
| title_full | Recognition of Vehicle License Plates Based on Image Processing |
| title_fullStr | Recognition of Vehicle License Plates Based on Image Processing |
| title_full_unstemmed | Recognition of Vehicle License Plates Based on Image Processing |
| title_short | Recognition of Vehicle License Plates Based on Image Processing |
| title_sort | recognition of vehicle license plates based on image processing |
| topic | deep learning license plate detection image processing SRGAN CCTV image |
| url | https://www.mdpi.com/2076-3417/11/14/6292 |
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