Research on Unconstrained Scene Multiple License Plate Recognition Method Based on Deep Learning
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.
万方数据
维普
