基于深度学习的车牌识别系统研究
Research on License Plate Recognition System Based on Deep Learning
孙小广 1万若楠1
作者信息
- 1. 广州城市理工学院 电子信息工程学院,广州 510800
- 折叠
摘要
本文针对目前车牌识别受限于光照、角度、场地等环境影响,开展基于深度学习的车牌识别研究.车牌定位前先对车牌图像进行灰度化处理、均值滤波消除噪声、边缘检测、二值化等预处理,结合几何、颜色等多项特征进行定位;再使用垂直投影法,找到每个字符的边界区域,逐一进行字符切割;最后搭建卷积神经网络,构建训练集和测试集,通过深度学习,识别和输出车牌字符,并在MATLAB上测试和仿真,准确率达到98.6%.
Abstract
To address the current limitations of license plate recognition due to environmental factors such as lighting,angle,and location,research on license plate recognition based on deep learning is constantly deepening.Before li-cense plate localization,the license plate image was preprocessed with grayscale processing,eliminating noise by mean filtering,edge detection,binarization,and multiple features such as geometry and color were combined for lo-calization;Then vertical projection method was used to find the boundary area of each character and perform character segmentation one by one;Finally,a convolutional neural network was created to construct a training and testing set.Through deep learning,license plate characters were recognized and output,and tested and validated on MATLAB with an accuracy of 98.6%.
关键词
车牌定位/车牌识别/卷积神经网络/深度学习Key words
license plate positioning/license plate recognition/CNN/deep learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据