基于深度学习的非约束场景多车牌识别方法研究

Research on Unconstrained Scene Multiple License Plate Recognition Method Based on Deep Learning

为解决非约束场景下的车牌识别精度问题,提出一种能够适应非约束场景并识别不同类型单车牌以及多车牌的自动车牌识别模型。该模型应用数据迁移技术,通过YOLOv5检测车辆并通过后处理筛选有效车辆目标,经检测并矫正后,通过ResNet18以及双向长短期记忆网络BLSTM网络结合连接时序分类损失CTC识别车牌字符。在模型训练过程中使用数据增强技术进一步提高了模型性能。该模型在CCPD以及AOLP的多个子数据集上进行了测试,展现出了优于其它方法识别精度和识别速度。

To solve the problem of license plate recognition accuracy in unconstrained scenarios,an automatic license plate recognition model that can adapt to unconstrained scenarios and recognize different types of single license plates and multiple license plates is proposed.The model uses data migration technology to detect vehicles through YOLOv5 and screen effective vehicle targets through post-processing.After detection and correction,the license plate characters are recognized through ResNet18 and BLSTM networks combined with CTC loss.Using data augmentation techniques during model training further improves model performance.After testing on multiple sub-datasets of CCPD and AOLP,the presented model shows better recognition accuracy and recognition speed than other methods.