A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing
With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving time...
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8913 |
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| author | Jiancai Leng Xinyi Chen Jinzhao Zhao Chongfeng Wang Jianqun Zhu Yihao Yan Jiaqi Zhao Weiyou Shi Zhaoxin Zhu Xiuquan Jiang Yitai Lou Chao Feng Qingbo Yang Fangzhou Xu |
| author_facet | Jiancai Leng Xinyi Chen Jinzhao Zhao Chongfeng Wang Jianqun Zhu Yihao Yan Jiaqi Zhao Weiyou Shi Zhaoxin Zhu Xiuquan Jiang Yitai Lou Chao Feng Qingbo Yang Fangzhou Xu |
| author_sort | Jiancai Leng |
| collection | DOAJ |
| description | With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge–LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge–LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network’s total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%. |
| first_indexed | 2024-03-11T11:21:37Z |
| format | Article |
| id | doaj.art-ab96ed902bd6483a8783e84db7441e67 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:37Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ab96ed902bd6483a8783e84db7441e672023-11-10T15:12:43ZengMDPI AGSensors1424-82202023-11-012321891310.3390/s23218913A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud ComputingJiancai Leng0Xinyi Chen1Jinzhao Zhao2Chongfeng Wang3Jianqun Zhu4Yihao Yan5Jiaqi Zhao6Weiyou Shi7Zhaoxin Zhu8Xiuquan Jiang9Yitai Lou10Chao Feng11Qingbo Yang12Fangzhou Xu13International School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaSchool of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaInternational School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, ChinaWith the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition (LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge–LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge–LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network’s total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.https://www.mdpi.com/1424-8220/23/21/8913license plate recognitionmodel compressionedge computingcloud computing |
| spellingShingle | Jiancai Leng Xinyi Chen Jinzhao Zhao Chongfeng Wang Jianqun Zhu Yihao Yan Jiaqi Zhao Weiyou Shi Zhaoxin Zhu Xiuquan Jiang Yitai Lou Chao Feng Qingbo Yang Fangzhou Xu A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing Sensors license plate recognition model compression edge computing cloud computing |
| title | A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing |
| title_full | A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing |
| title_fullStr | A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing |
| title_full_unstemmed | A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing |
| title_short | A Light Vehicle License-Plate-Recognition System Based on Hybrid Edge–Cloud Computing |
| title_sort | light vehicle license plate recognition system based on hybrid edge cloud computing |
| topic | license plate recognition model compression edge computing cloud computing |
| url | https://www.mdpi.com/1424-8220/23/21/8913 |
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