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Apache-2.0

Yolov3_LPRnet_pytorch

(如果你使用git下载总是报红,你可以选择下载dev-sidecar编程软件加速下载--https://gitee.com/interesting-goods/dev-sidecar?_from=gitee_search)

国内gitee仓库地址: https://gitee.com/wucaip/Yolov3_LPRnet_pytorch

一、基于yolov3的LPRnet车牌识别

以yolov3作文检测器、LPRnet作为识别模型,实现了实时车牌车牌识别模型。本项目直接使用LPRnet原始的网络作为车牌识别模型。

二、数据集介绍

本项目使用的是CCPD2019数据集中的ccpd_base常用车牌数据集作为训练集,所以只能识别蓝底的中国车牌。

三、模型预测

1.下载模型(下载解压,放在model_data文件夹中)

链接:https://pan.baidu.com/s/1sARkEgcWt4tpatAqGLXWEA 提取码:6eu6

2.CMD进入命令行模式,运行 predict.py 预测脚本

python  predict.py

输入预测图片的地址: img/test.jpg


原始图片

识别后的图片

四、检测模型训练

1.下载数据集,并解压后,将CCPD2019文件夹整个文件复制到本目录的CCPD2019中

链接:https://pan.baidu.com/s/1QdNG-iqIhZzSWdlOvS9gxQ 提取码:ymd3

2.解压标签

解压CCPD2019中的lable.zip脚本

3.下载模型(下载解压,放在model_data文件夹中)

链接:https://pan.baidu.com/s/1sARkEgcWt4tpatAqGLXWEA 提取码:6eu6

4.运行训练脚本

python train.py

五、目录介绍

Project:

│ predict.py # 预测脚本

│ README.md

│ requirements.txt # 本项目所需要的库

│ train.py # 训练脚本

│ voc_annotation.py # 对Voc数据集预测里脚本

├─img

│ test.jpg

├─model_data

│ ep010-loss1.900-val_loss1.586.pth # yolo网络模型

│ Final_LPRNet_model.pth # LPRNet网络模型

│ simhei.ttf # 预测画框的字体文件

│ my_classes.txt # 数据集种类文本

│ yolo_anchors.txt # anchor文件

├─CCPD2019

│ transformer.py #CCPD2019生成标签的脚本

├─nets

│ │ darknet.py # yolo骨干网络

│ │ loss.py # yolo的损失函数脚本

│ │ yolonet.py # yolo整体网络

├─LPRNet

│ │ LPRNet.py # 车牌识别骨干网络

└─utils

│  dataloader.py    # 加载数据集以及预处理脚本

│  predict_yolo.py  # yolo模型预测函数脚本

│  predict_LPRNet.py# 车牌识别模型预测函数脚本

│  utils.py         # 真个项目需要用到的函数脚本

│  utils_bbox.py    # 模型预测的预测框解码以及nms函数脚本

│  utils_fit.py     # 训练函数脚本

六、待改进

  • 使用CCPD2019全部数据集+CCPD2020数据集进行训练
  • 使用CCPD2019+CCPD2020数据集重新训练LPRnet网络

七、参考

  1. https://github.com/sirius-ai/LPRNet_Pytorch
  2. https://github.com/bubbliiiing/yolo3-pytorch
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简介

基于yolov3的LPRnet车牌识别。以yolov3作文检测器、LPRnet作为识别模型,实现了实时车牌车牌识别模型。本项目直接使用LPRnet原始的网络作为车牌识别模型。 展开 收起
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