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基于云边缘协同计算的表面缺陷检测系统研究

Research on a Surface Defect Detection System Based on the Cloudedge Computing
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摘要 基于现有表面缺陷检测系统所存在的实时检测难、硬件要求高等问题,提出一种基于云计算与边缘协同计算的表面缺陷检测系统。将轻量化改进后的YOLOv4缺陷检测算法模型部署到边缘端嵌入式设备中,在边缘端完成对表面缺陷的检测,并在边缘端和云端设备部署KubeEdge框架进行通信和管理。通过案例验证该系统不仅能够满足检测实时性的要求,还能够提取缺陷检测关键信息,同时便于部署在价格低廉的嵌入式设备。 Existing surface defect detection system has a series of problems such as the difficulty of real timedetection and the high hardware requirements.To solve these problems,a surface defect detection system is proposed using the cloudedge computing.The system deploys the lightweight YOLOv4 defect detection algorithm model to the edgeend embedded device,completes the detection of surface defects at the edgeend,and deploys the KubeEdge framework on the edge and cloud devices for communication and management.Through case verification,the proposed system can not only meet the requirements of realtime detection,but also extract key information about defect detection,and is easy to deploy in lowcost embedded devices.
作者 梁程 薛建彬 LIANG Cheng;XUE Jianbin(College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处 《机械与电子》 2022年第2期65-70,共6页 Machinery & Electronics
关键词 云计算 边缘计算 表面缺陷检测 深度学习算法 cloud computing edge computing surface defect detection deep learning algorithm
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