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旨在打造一个帮助船政部门进行渔船违法走私船只的监测,利用OCR技术进行模型的训练,并且部署到Jetson上面进行推理,并将推理结果传回云服务器,同时通过PyQt5打造一个方便监测的前端界面,并且和云服务器连通,可实现随时监测!

License: MIT License

Python 53.21% Makefile 0.03% C++ 46.76%

ship-num-plate-detect-system's Introduction

SNPDS-智慧船政-基于深度学习的智能船牌检测系统

项目背景

随着科技发展,我们的大街小巷上已经遍布了我们的“天网”摄像头,并且有些摄像头能自动进行车辆或者行人追踪,有些也可以做到车牌识别等等。但是在我们的江河江河之上却远远没有做到。**是世界上河流最多的国家之一。**有许多源远流长的大江大河。其中流域面积超过1000平方公里的河流就有2221条。随着环境保护,和禁渔期的进一步推动,则对渔船以及不法运输船只的监控难度则大大增加,我们基于百度PaddleOCR,利用对船牌的识别,监控以及对非法船只预警等一系列自动化流程,我们相信能大大降低我国船政部门的工作压力,能有效提高工作效率,从而对船只进行更好的管理。

项目结构

项目分为四个部分:训练数据集得到模型模型部署搭建服务器监听构建客户端监测。其中目录代码结构为:

---SNPDS_Nano_Client  #nano部署的推理客户端
---SNPDS_PC_Client  #电脑端监测的前端界面
---SNPDS_Server     #服务端负责转发的代码

系统构思结构:

image-20210722224252900

项目效果

  • 本地PC客户端效果

image-20210722225301461

image-20210722225957098

  • 实际效果

image-20210730212113467

ship-num-plate-detect-system's People

Contributors

miller-em avatar

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