{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:28:27Z","timestamp":1762432107952,"version":"3.37.3"},"reference-count":34,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,11,20]],"date-time":"2021-11-20T00:00:00Z","timestamp":1637366400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019054","name":"Changsha Science and Technology Project","doi-asserted-by":"publisher","award":["kq1901139","2016WK2023"],"award-info":[{"award-number":["kq1901139","2016WK2023"]}],"id":[{"id":"10.13039\/501100019054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019081","name":"Science and Technology Project of Hunan Province","doi-asserted-by":"crossref","award":["kq1901139","2016WK2023"],"award-info":[{"award-number":["kq1901139","2016WK2023"]}],"id":[{"id":"10.13039\/501100019081","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2021,11,20]]},"abstract":"<jats:p>The license plate recognition is an important part of the intelligent traffic management system, and the application of deep learning to the license plate recognition system can effectively improve the speed and accuracy of recognition. Aiming at the problems of traditional license plate recognition algorithms such as the low accuracy, slow speed, and the recognition rate being easily affected by the environment, a Convolutional Neural Network- (CNN-) based license plate recognition algorithm-Fast-LPRNet is proposed. This algorithm uses the nonsegment recognition method, removes the fully connected layer, and reduces the number of parameters. The algorithm\u2014which has strong generalization ability, scalability, and robustness\u2014performs license plate recognition on the FPGA hardware. Increaseing the depth of network on the basis of the Fast-LPRNet structure, the dataset of Chinese City Parking Dataset (CCPD) can be recognized with an accuracy beyond 90%. The experimental results show that the license plate recognition algorithm has high recognition accuracy, strong generalization ability, and good robustness.<\/jats:p>","DOI":"10.1155\/2021\/8592216","type":"journal-article","created":{"date-parts":[[2021,11,20]],"date-time":"2021-11-20T20:05:05Z","timestamp":1637438705000},"page":"1-11","source":"Crossref","is-referenced-by-count":6,"title":["Research and Implementation of Fast-LPRNet Algorithm for License Plate Recognition"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2325-4348","authenticated-orcid":true,"given":"Zhichao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"given":"Jiaxin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"given":"Siyu","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"given":"Jian","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5447-7312","authenticated-orcid":true,"given":"Feng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China"}]}],"member":"311","reference":[{"key":"1","article-title":"Recycling waste classification using optimized convolutional neural network","volume":"164","author":"M. 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