一种融合移动边缘计算和深度学习的城市街道垃圾检测和清洁度评估方法

Urban Street Garbage Detection and Cleanliness Assessment Approach Fusing Mobile Edge Computing and Deep Learning

针对近年来城市化进程的加快,城市街道垃圾的随意出现给市政部门及时清扫带来巨大困难.在移动、云技术和物联网飞速发展的背景下,论文研究并提出了一种融合移动边缘计算和深度学习的城市街道垃圾检测和清洁度评估方法,通过安装在车辆上的高分辨率摄像机也称为"移动站"和手持的移动设备进行街景图象收集,利用边缘服务器临时存储并处理街景图象信息,然后通过城市网络把这些数据传输到云中心,利用深度学习技术识别街道垃圾类别以及对垃圾数量计数,并且将这些结果引入到基于层次的街道清洁度评估框架当中,最终可视化街道清洁度等级,为城市市政管理者有效安排清理人员提供方便.文章最后基于南京市江宁区的街道图像,可视化了江宁区街道清洁度等级.实际应用初步验证了其可行性和可用性.

In response to the acceleration of the urbanization process in recent years,the random appearance of urban street garbage has brought great difficulties to the municipal departments in timely cleaning. Under the background of the rapid development of mobile,cloud technology and the Internet of Things,this paper studies and proposes an approach for urban street garbage detection and cleanliness assessment fusing mobile edge computing and deep learning. The high resolution cameras installed on vehicles collect street viewimages,which is also called " mobile station". Using edge servers to store temporarily and extracting street viewimage location information,then transmitting these data to the cloud center for analysis through city networks. At the same time,deep learning technology is used to identify street garbage categories and counting the number of garbage. These results are incorporated into the street cleanliness calculation framework to ultimately visualize street cleanliness levels,which provides convenience for city municipal managers to arrange clean-up personnel effectively. At the end of the paper,the street cleanliness level of Jiangning District was visualized based on the street image of Jiangning District in Nanjing. The practical application verifies the feasibility and availability of the system preliminarily.