License Plate Detection with Shallow and Deep CNNs in Complex Environments
License plate detection is a challenging problem due to the large visual variations in complex environments, such as motion blur, occlusion, and lighting changes. An advanced discriminative model is needed to accurately segment license plates from the backgrounds. However, effective models for the p...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/7984653 |
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| _version_ | 1832552740064067584 |
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| author | Li Zou Meng Zhao Zhengzhong Gao Maoyong Cao Huarong Jia Mingtao Pei |
| author_facet | Li Zou Meng Zhao Zhengzhong Gao Maoyong Cao Huarong Jia Mingtao Pei |
| author_sort | Li Zou |
| collection | DOAJ |
| description | License plate detection is a challenging problem due to the large visual variations in complex environments, such as motion blur, occlusion, and lighting changes. An advanced discriminative model is needed to accurately segment license plates from the backgrounds. However, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose to detect license plate based on two CNNs, a shallow CNN and a deep CNN. The shallow CNN is used to quickly remove most of the background regions to reduce the computation cost, and the deep CNN is used to detect license plate in the remaining regions. These two CNNs are trained end to end and are complementary to each other to guarantee the detection precision with low computation cost. Experimental results show that the proposed method is promising for license plate detection. |
| format | Article |
| id | doaj-art-ca624647fdcf4c28a8f6c0d8c8cd7723 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ca624647fdcf4c28a8f6c0d8c8cd77232025-02-03T05:58:02ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/79846537984653License Plate Detection with Shallow and Deep CNNs in Complex EnvironmentsLi Zou0Meng Zhao1Zhengzhong Gao2Maoyong Cao3Huarong Jia4Mingtao Pei5College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaBeijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing 100081, ChinaBeijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing 100081, ChinaLicense plate detection is a challenging problem due to the large visual variations in complex environments, such as motion blur, occlusion, and lighting changes. An advanced discriminative model is needed to accurately segment license plates from the backgrounds. However, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose to detect license plate based on two CNNs, a shallow CNN and a deep CNN. The shallow CNN is used to quickly remove most of the background regions to reduce the computation cost, and the deep CNN is used to detect license plate in the remaining regions. These two CNNs are trained end to end and are complementary to each other to guarantee the detection precision with low computation cost. Experimental results show that the proposed method is promising for license plate detection.http://dx.doi.org/10.1155/2018/7984653 |
| spellingShingle | Li Zou Meng Zhao Zhengzhong Gao Maoyong Cao Huarong Jia Mingtao Pei License Plate Detection with Shallow and Deep CNNs in Complex Environments Complexity |
| title | License Plate Detection with Shallow and Deep CNNs in Complex Environments |
| title_full | License Plate Detection with Shallow and Deep CNNs in Complex Environments |
| title_fullStr | License Plate Detection with Shallow and Deep CNNs in Complex Environments |
| title_full_unstemmed | License Plate Detection with Shallow and Deep CNNs in Complex Environments |
| title_short | License Plate Detection with Shallow and Deep CNNs in Complex Environments |
| title_sort | license plate detection with shallow and deep cnns in complex environments |
| url | http://dx.doi.org/10.1155/2018/7984653 |
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