Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

Xiufeng Li, Zheng Wen, Qiaozhi Hua*

*この研究の対応する著者

研究成果: Article査読

抄録

Intelligent transportation applications based on urban Internet of Things can improve the efficiency of government services and promote urban modernization. As smart cameras are more and more widely used in cities, artificial intelligence technology is an important force to achieve license plate recognition. An efficient license plate recognition algorithm not only improves the efficiency of traffic management but also saves management costs. This paper proposes a network based on the shufflenetv2 dilated convolution (SDC) model, which includes two parts: license plate location and license plate recognition. SDC model adopts shufflenetv2 as the backbone network, which combines dilated convolution and global context blocks. Therefore, the receptive field and feature expression ability of the model are enhanced. For license plate location, CIOU loss considers not only the coverage area of the bounding box but also the center distance and aspect ratio. For license plate recognition, CTC loss trains the network based on the sequence and solves the sample alignment problem, which improves the accuracy of license plate recognition. The experiments show that the precision of the SDC model in license plate location is 98.7%, which is 5.2%, 5.5%, and 4.1% higher than the precision of Faster-RCNN, YOLOv3, and SSD, respectively. The precision of the SDC model in license plate recognition is 98.2%, which is 5.3%, 3.7%, and 2.9% higher than the precision of LPRNet, AlexNet, and RPNet, respectively.

本文言語English
論文番号3627246
ジャーナルWireless Communications and Mobile Computing
2022
DOI
出版ステータスPublished - 2022

ASJC Scopus subject areas

  • 情報システム
  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

フィンガープリント

「Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル