• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
成果搜索

author:

Ke, Xiao (Ke, Xiao.) [1] | Zeng, Ganxiong (Zeng, Ganxiong.) [2] | Guo, Wenzhong (Guo, Wenzhong.) [3]

Indexed by:

EI

Abstract:

Recently, with the development of deep learning, Automatic License Plate Recognition (ALPR) has made great progress, However, there are still many challenges to accomplish license plate (LP) recognition under various traffic scenarios. One of them is the detection speed and recognition speed, and the other is the difficulty to recognize the low resolution and highly tilted LP images. In this paper, we present a two-stage ALPR framework to achieve efficient LP detection and recognition in unconstrained scenarios. Our LP detector is based on improved Yolov3-tiny, and we also propose a lightweight recognition network MRNet based on multi-scale features. In order to improve the inference speed, we abandoned the rectification of LP images, and RNNs that are difficult to compute in parallel. Additionally, we also propose a license plate data augmentation method, which achieves more effective augmentation and improves the generalization ability of the network through secondary random and hyperparametric search. As an additional contribution, we provide a challenging dataset collected from real-world driving recorders. The dataset is for multiple LPs in a single image, which makes up for the lack of public datasets in multiple LP scenarios. We evaluated the results on five datasets and showed that we achieved the best performance on almost all test sets, achieving 99.8% accuracy on CCPD with more than 180,000 license plate test sets. In terms of speed, the inference speed for detecting license plates reaches 751 FPS, and the fastest inference time for recognizing a single license plate takes only 2.9 ms. © 2000-2011 IEEE.

Keyword:

Deep learning Feature extraction Image enhancement Image recognition Job analysis License plates (automobile) Object detection Object recognition Optical character recognition

Community:

  • [ 1 ] [Ke, Xiao]Fuzhou University, Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou; 350116, China
  • [ 2 ] [Ke, Xiao]Ministry of Education, Key Laboratory of Spatial Data Mining and Information Sharing, Fuzhou; 350003, China
  • [ 3 ] [Zeng, Ganxiong]Fuzhou University, Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou; 350116, China
  • [ 4 ] [Zeng, Ganxiong]Ministry of Education, Key Laboratory of Spatial Data Mining and Information Sharing, Fuzhou; 350003, China
  • [ 5 ] [Guo, Wenzhong]Fuzhou University, Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, College of Computer and Data Science, Fuzhou; 350116, China
  • [ 6 ] [Guo, Wenzhong]Ministry of Education, Key Laboratory of Spatial Data Mining and Information Sharing, Fuzhou; 350003, China

Reprint 's Address:

Email:

Show more details

Related Keywords:

Related Article:

Source :

IEEE Transactions on Intelligent Transportation Systems

ISSN: 1524-9050

Year: 2023

Issue: 5

Volume: 24

Page: 5172-5185

7 . 9

JCR@2023

7 . 9 0 0

JCR@2023

ESI HC Threshold:35

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 10

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

Affiliated Colleges:

Online/Total:120/10011465
Address:FZU Library(No.2 Xuyuan Road, Fuzhou, Fujian, PRC Post Code:350116) Contact Us:0591-22865326
Copyright:FZU Library Technical Support:Beijing Aegean Software Co., Ltd. 闽ICP备05005463号-1