基于Mesh网络和深度学习的城市智慧停车

Design of urban smart parking with Mesh network and deep learning

近些年来,随着城市的发展,“停车难”的问题在我国不少地方都比较突出。传统的人工记时以及在出入口刷卡进出等方式费时费力,且拓展能力不足。基于物联网、人工智能和大数据等技术,可以对现有的停车管理理念和方式进行升级,通过加入更多的智能化应用和无人操作可以提高停车场的运行效率和监控水平,并同时为智慧城市管理提供停车大数据。文中提出一种融合Mesh网络、超分辨率图像重建、车辆识别等技术的设计方案,并配合相应的硬件模块验证了方案的可行性。

In recent years,with the development of cities,the problem of"difficult parking"has become more popular in many places in China.Traditional manual operation or card swiping in and out waste a lot of time and work,and lack of expansion capacity.Based on new technologies such as Internet of Things,Artificial Intelligence(AI)and big data,can change parking lot management concepts and methods,improve the operation efficiency and monitoring level of parking lot by adding more intelligent application and unmanned operation,then provide parking big data for smart city management at the same time.This paper proposes a design scheme of Mesh network,super⁃resolution image reconstruction,vehicle identification and verified by using corresponding hardware modules.