FAFEnet: A fast and accurate model for automatic license plate detection and recognition
Abstract Automatic License Plate detection and Recognition (ALPR) is a key problem in intelligent transportation systems with wide applications in traffic monitoring, electronic toll collection (ETC), intelligent parking lots (IPLs), and elsewhere. Although numerous methods have been proposed in the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-02-01
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| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12674 |
| _version_ | 1797903405711097856 |
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| author | Xin Zhou Yao Cheng Liling Jiang Bo Ning Yanhao Wang |
| author_facet | Xin Zhou Yao Cheng Liling Jiang Bo Ning Yanhao Wang |
| author_sort | Xin Zhou |
| collection | DOAJ |
| description | Abstract Automatic License Plate detection and Recognition (ALPR) is a key problem in intelligent transportation systems with wide applications in traffic monitoring, electronic toll collection (ETC), intelligent parking lots (IPLs), and elsewhere. Although numerous methods have been proposed in the literature, it is still challenging to strike a good balance between the accuracy and efficiency of ALPR. In this paper, a novel end‐to‐end CNN‐based model is proposed, called Fast and Accurate Network with Feature Enhancement (FAFEnet), to jointly detect the license plates and recognize the characters with high accuracy and efficiency. Specifically, the FAFEnet model seamlessly integrates two CNN‐based models, namely the detection and recognition modules, into a unified framework to reduce accumulated errors and computational overheads in two separate steps. The detection module is a lightweight model with only seven convolutional layers yet achieves over 99.8% accuracy rates for license plate detection across all datasets. The recognition module utilizes two feature enhancement blocks to compensate and enhance the shallow character features extracted by the detection module. Furthermore, the joint optimization of detection and recognition modules exploits the feature association in two modules, and thus improves the prediction accuracy while reducing the execution time. Finally, extensive experimental results on several real‐world datasets demonstrate that FAFEnet outperforms all the competitors in terms of both accuracy and efficiency. |
| first_indexed | 2024-04-10T09:32:22Z |
| format | Article |
| id | doaj.art-cca609884559459ba87ef1ac182076a8 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-10T09:32:22Z |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-cca609884559459ba87ef1ac182076a82023-02-19T04:18:32ZengWileyIET Image Processing1751-96591751-96672023-02-0117380781810.1049/ipr2.12674FAFEnet: A fast and accurate model for automatic license plate detection and recognitionXin Zhou0Yao Cheng1Liling Jiang2Bo Ning3Yanhao Wang4School of Information Science and Technology Dalian Maritime University Dalian ChinaSchool of Data Science and Engineering East China Normal University Shanghai ChinaSchool of Information Science and Technology Dalian Maritime University Dalian ChinaSchool of Information Science and Technology Dalian Maritime University Dalian ChinaSchool of Data Science and Engineering East China Normal University Shanghai ChinaAbstract Automatic License Plate detection and Recognition (ALPR) is a key problem in intelligent transportation systems with wide applications in traffic monitoring, electronic toll collection (ETC), intelligent parking lots (IPLs), and elsewhere. Although numerous methods have been proposed in the literature, it is still challenging to strike a good balance between the accuracy and efficiency of ALPR. In this paper, a novel end‐to‐end CNN‐based model is proposed, called Fast and Accurate Network with Feature Enhancement (FAFEnet), to jointly detect the license plates and recognize the characters with high accuracy and efficiency. Specifically, the FAFEnet model seamlessly integrates two CNN‐based models, namely the detection and recognition modules, into a unified framework to reduce accumulated errors and computational overheads in two separate steps. The detection module is a lightweight model with only seven convolutional layers yet achieves over 99.8% accuracy rates for license plate detection across all datasets. The recognition module utilizes two feature enhancement blocks to compensate and enhance the shallow character features extracted by the detection module. Furthermore, the joint optimization of detection and recognition modules exploits the feature association in two modules, and thus improves the prediction accuracy while reducing the execution time. Finally, extensive experimental results on several real‐world datasets demonstrate that FAFEnet outperforms all the competitors in terms of both accuracy and efficiency.https://doi.org/10.1049/ipr2.12674 |
| spellingShingle | Xin Zhou Yao Cheng Liling Jiang Bo Ning Yanhao Wang FAFEnet: A fast and accurate model for automatic license plate detection and recognition IET Image Processing |
| title | FAFEnet: A fast and accurate model for automatic license plate detection and recognition |
| title_full | FAFEnet: A fast and accurate model for automatic license plate detection and recognition |
| title_fullStr | FAFEnet: A fast and accurate model for automatic license plate detection and recognition |
| title_full_unstemmed | FAFEnet: A fast and accurate model for automatic license plate detection and recognition |
| title_short | FAFEnet: A fast and accurate model for automatic license plate detection and recognition |
| title_sort | fafenet a fast and accurate model for automatic license plate detection and recognition |
| url | https://doi.org/10.1049/ipr2.12674 |
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