北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (5): 93-99.

• 论文 • 上一篇    下一篇

一种识别倾斜混合颜色车牌的轻量级模型

向万里,王力东,孟学雷,杜文举,杨霄煜   

  1. 兰州交通大学
  • 收稿日期:2023-08-29 修回日期:2023-12-03 出版日期:2024-10-28 发布日期:2024-11-10
  • 通讯作者: 向万里 E-mail:xiangwl@lzjtu.edu.cn
  • 基金资助:
    国家自然科学基金

A Lightweight Model for Identifying Tilted Mixed Color License Plates

  • Received:2023-08-29 Revised:2023-12-03 Online:2024-10-28 Published:2024-11-10
  • Contact: Wan-Li XIANG E-mail:xiangwl@lzjtu.edu.cn

摘要: 目前,基于神经网络的车牌识别模型尺寸较大,不利于部署在边缘计算设备,此外,对倾斜类车牌和混合颜色车牌识别率偏低。为此,提出一种识别倾斜混合颜色车牌的轻量级神经网络组合模型。该模型首先采用基于YOLO算法改进的P-YOLO算法实现车牌的检测、分类和定位;然后采用基于LPRNet算法改进的N-G-LPRNet算法识别车牌字符。最后对比改进前算法,在CCPD数据集上的测试结果表明:在车牌检测阶段P-YOLO算法检测倾斜类车牌的mAP指标大幅提高,结合字符识别网络,对常规车牌、微倾斜车牌和强倾斜车牌识别准确率分别提高约1.3%、70.7%和63.8%;在字符识别阶段,混合颜色车牌训练的前提下,N-G-LPRNet算法对蓝色车牌和绿色车牌的识别率分别提升约40.65%和32.26%;最终形成的P-YOLO-N-G-LPRNet组合模型综合识别率达到98.16%,模型占用空间约为5MB,在高识别率和轻量化方面优势明显。

关键词: 深度学习, 目标检测, 字符识别, YOLO, LPRNet

Abstract: At present, the license plate recognition model based on neural network is large, which is not conducive to deployment in edge computing equipment. In addition, the recognition rate of oblique license plates and mixed color license plates is low. To this end, a lightweight neural network combination model for recognizing tilted mixed color license plates is proposed. The model first uses the improved P-YOLO algorithm based on YOLO algorithm to achieve license plate detection, classification, and localization; Then, an improved N-G-LPRNet algorithm based on LPRNet algorithm is used to recognize license plate characters. Finally, compared with the improved algorithm model, the test results on the CCPD dataset show that the P-YOLO algorithm significantly improves the mAP index for detecting tilted license plates during the license plate detection stage. Combined with character recognition networks, the recognition accuracy for conventional license plates, slightly tilted license plates, and strongly tilted license plates is improved by about 1.3%, 70.7%, and 63.8%, respectively; In the character recognition stage, under the premise of mixed color license plate training, the N-G-LPRNet algorithm improves the recognition rates of blue and green license plates by about 40.65% and 32.26%, respectively; The final P-YOLO-N-G-LPRNet combination model has a comprehensive recognition rate of 98.16%, occupying approximately 5MB of space. It has obvious advantages in high recognition rate and lightweight.

Key words: Deep learning, Target detection, Character recognition, YOLO, LPRNet

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