摘要
随着理计计重成为钢贸交易的主流计重方式,快速精准的钢筋智能计数系统成为研究热点。提出了基于边云协同架构的钢筋视觉计数系统设计方案。该系统利用移动端拍摄钢筋影像,对影像进行背景剔除、尺寸调整等预处理后上传至云端;云端利用一种改进的YOLOv5钢筋检测模型对影像中的钢筋进行智能检测,并将结果反馈至移动端;移动端经过人工检查修正后,即可得到高可信度的计数结果。根据在SPDC库的测试结果,系统的计数正确率可达99.85%,高于人工计数的平均正确率;每千根钢筋计数时间为26.50 s,显著低于人工计数的平均时间。
Theoretical weight is the mainstream weight calculation method in current steel trade,thus a fast and accurate AI based rebar counting system has become a critical research issue.A design scheme of visual rebar counting system based on cloud-edge collaboration is proposed.The system uses a mobile terminal to capture rebar images,performs preprocessing including background removal and size adjustment,and uploads them to the cloud;The cloud uses a detection model based on an improved YOLOv5 to detect and count the rebars in the image,and feeds back the counting results to the mobile terminal.According to the test results in the SPDC,the counting accuracy of the proposed system can reach 99.85%after manual correction,which is higher than the average accuracy of manual counting.The time cost of intelligent counting per 1000 rebars is 26.50 seconds after manual correction,significantly lower than the average time of manual counting.
作者
谢方立
李正浩
赵迅逸
张政
郑力菥
林熙翔
李向东
XIE Fangli;LI Zhenghao;ZHAO Xunyi;ZHANG Zheng;ZHENG Lixi;LIN Xixiang;LI Xiangdong(Chongqing Institute of Engineering,Chongqing 400056,China;Chongqing School,University of Chinese Academy of Sciences,Chongqing 400714,China;Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,China;Chongqing Infopro Technology Co.,Ltd,Chongqing 404100,China)
出处
《激光杂志》
CAS
北大核心
2024年第8期203-207,共5页
Laser Journal
基金
国家重点研发计划(No.2018YFC0823503)
国家自然科学基金(No.62106247)。
关键词
人工智能
钢筋计数
边云协同
YOLO
artificial intelligence
rebar counting
cloud-edge collaboration
YOLO
作者简介
谢方立(1995-),男,硕士,主要研究方向:软件开发、计算机视觉;通讯作者:李正浩(1980-),男,博士,副研究员,硕士生导师,主要研究方向:计算机视觉、机器学习和边缘计算。E-mail:lizh@cigit.ac.cn。
