中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (3): 1-14.doi: 10.19682/j.cnki.1005-8885.2022.1014

• •    下一篇

Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system

  

  1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
  • 收稿日期:2022-02-28 修回日期:2022-06-22 出版日期:2022-06-30 发布日期:2022-06-30
  • 通讯作者: 贾伟 E-mail:jiawei@hfut.edu.cn
  • 基金资助:
    This work was supported by the National Natural Science Foundation of China (62076086), and the Key Research and
    Development Program in Anhui Province (202004d07020008).

Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system

  1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
  • Received:2022-02-28 Revised:2022-06-22 Online:2022-06-30 Published:2022-06-30
  • Contact: Jia Wei E-mail:jiawei@hfut.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (62076086), and the Key Research and
    Development Program in Anhui Province (202004d07020008).

摘要: In this paper, the performance of you only look once ( YOLO) series detectors on Chinese license plate recognition (LPR) in the real intelligent transportation system (ITS) monitoring scene is investigated. Specially, a precise and efficient automatic license plate recognition ( ALPR ) system based on the YOLOv4 detector is proposed. The proposed ALPR system contains three stages including vehicle detection, license plate detection (LPD) and LPR. In vehicle detection stage, YOLOv4 detector is directly applied. In LPD stage, YOLOv4-tiny detector is exploited. In the last stage, the YOLOv4-tiny detector with attention mechanism for LPR is proposed to use. In addition, a large Chinese license plate dataset containing 10 500 images collected from all 31 provinces in the Chinese mainland is created. This Chinese license plate dataset is named Hefei University of Technology license plate version 1 (HFUT-LP v1). Particularly, HFUT-LP v1 dataset is collected in the real ITS monitoring scene. In order to compare the performance of different object detection algorithms for ALPR, a variety of object detection algorithms are used to make a comprehensive performance evaluation. Experimental results show that the proposed ALPR system achieves very high accuracy and has very fast processing speed, which is suitable for real-time LPR.

关键词: license plate detection(LPD), license plate recognition(LPR), YOLOv4-tiny detector, attention mechanism, intelligent transportation

Abstract: In this paper, the performance of you only look once ( YOLO) series detectors on Chinese license plate recognition (LPR) in the real intelligent transportation system (ITS) monitoring scene is investigated. Specially, a precise and efficient automatic license plate recognition ( ALPR ) system based on the YOLOv4 detector is proposed. The proposed ALPR system contains three stages including vehicle detection, license plate detection (LPD) and LPR. In vehicle detection stage, YOLOv4 detector is directly applied. In LPD stage, YOLOv4-tiny detector is exploited. In the last stage, the YOLOv4-tiny detector with attention mechanism for LPR is proposed to use. In addition, a large Chinese license plate dataset containing 10 500 images collected from all 31 provinces in the Chinese mainland is created. This Chinese license plate dataset is named Hefei University of Technology license plate version 1 (HFUT-LP v1). Particularly, HFUT-LP v1 dataset is collected in the real ITS monitoring scene. In order to compare the performance of different object detection algorithms for ALPR, a variety of object detection algorithms are used to make a comprehensive performance evaluation. Experimental results show that the proposed ALPR system achieves very high accuracy and has very fast processing speed, which is suitable for real-time LPR.

Key words: license plate detection(LPD), license plate recognition(LPR), YOLOv4-tiny detector, attention mechanism, intelligent transportation

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