文献类型：期刊文献

英文题名：Computer Vision System for Multi-Robot Construction Waste Management: Integrating Cloud and Edge Computing

作者：Wang, Zeli[1];Yang, Xincong[2];Zheng, Xianghan[3];Huang, Daoyin[4];Jiang, Binfei[4]

机构：[1]East China Univ Sci & Technol, Dept Management Sci & Engn, Shanghai 200030, Peoples R China;[2]Harbin Inst Technol, Sch Civil & Environm Engn, Shenzhen 518055, Peoples R China;[3]Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350108, Peoples R China;[4]Putian Digital Grp Co Ltd, Putian 351100, Peoples R China

年份：2024

卷号：14

期号：12

外文期刊名：BUILDINGS

收录：;EI(收录号：20245217603109);WOS:【SCI-EXPANDED(收录号:WOS:001387178800001)】；

基金：This research was funded by "Nature Science Foundation of China, grant number 72304098", "Regional Development Project of Fujian Provincial Department of Science and Technology, grant number 2023H4007" and "Shanghai Pujiang Program, grant number 22PJC030".

语种：英文

外文关键词：construction waste management; waste recycling; multi-robot; computer vision; edge computing; cloud computing

摘要：Sorting is an important construction waste management tool to increase recycling rates and reduce pollution. Previous studies have used robots to improve the efficiency of construction waste recycling. However, in large construction sites, it is difficult for a single robot to accomplish the task quickly, and multiple robots working together are a better option. Most construction waste recycling robotic systems are developed based on a client-server framework, which means that all robots need to be continuously connected to their respective cloud servers. Such systems are low in robustness in complex environments and waste a lot of computational resources. Therefore, in this paper, we propose a pixel-level automatic construction waste recognition platform with high robustness and low computational resource requirements by combining multiple computer vision technologies with edge computing and cloud computing platforms. Experiments show that the computing platform proposed in this study can achieve a recognition speed of 23.3 fps and a recognition accuracy of 90.81% at the edge computing platform without the help of network and cloud servers. This is 23 times faster than the algorithm used in previous research. Meanwhile, the computing platform proposed in this study achieves 93.2% instance segmentation accuracy on the cloud server side. Notably, this system allows multiple robots to operate simultaneously at the same construction site using only a single server without compromising efficiency, which significantly reduces costs and promotes the adoption of automated construction waste recycling robots.