本文已被：浏览 554次 下载 1526次
Received:August 03, 2021 Revised:August 31, 2021
Received:August 03, 2021 Revised:August 31, 2021
中文摘要: 传统的车间巡检方法主要依靠人工检查及记录结果, 过程繁琐且无法实时共享全过程. 为了提高工作效率, 将深度学习应用到混合现实车间巡检中. 采用了深度学习与混合现实技术相结合的方式, 利用ResNet网络对车间设备进行分类识别, 完成分类识别后, 再利用HoloLens的空间感知能力定位到该设备进行确认, 最后显示该设备的基本信息、运行状态和报警等信息. 经实验验证, 与传统的车间巡检方法相比, 具有较高识别率的ResNet有效地过滤了噪声, 提高了HoloLens的利用率和识别率, 同时也提高了巡检人员的工作效率.
Abstract:The traditional method of workshop inspection mainly relies on manual check and recording, which is cumbersome and cannot be shared in real time. For higher work efficiency, deep learning is applied to mixed reality workshop inspection. It is combined with mixed reality technology, and the ResNet network is used to classify and identify workshop equipment. After classification and identification, HoloLens’ spatial perception ability is leveraged to locate and confirm the equipment. Finally, equipment basic information, operating status, and alarms are displayed. The experimental results show that compared with traditional workshop inspection methods, ResNet, with a high identification rate, can effectively filter noises, improve the utilization rate and identification rate of HoloLens, and consequently improve the work efficiency of inspection personnel.
keywords: mixed reality ResNet deep learning workshop inspection HoloLens convolutional neural network (CNN)
文章编号： 中图分类号： 文献标志码：
基金项目:辽宁省教育厅2021年度科学研究经费项目(LJKZ0434); 沈阳市重大科技成果转化专项(20-203-5-40)
引用文本：
刘云江,关慧,王鸿亮,王继娜.深度学习在混合现实车间巡检中的应用.计算机系统应用,2022,31(5):118-123
LIU Yun-Jiang,GUAN Hui,WANG Hong-Liang,WANG Ji-Na.Application of Deep Learning in Mixed Reality Workshop Inspection.COMPUTER SYSTEMS APPLICATIONS,2022,31(5):118-123
刘云江,关慧,王鸿亮,王继娜.深度学习在混合现实车间巡检中的应用.计算机系统应用,2022,31(5):118-123
LIU Yun-Jiang,GUAN Hui,WANG Hong-Liang,WANG Ji-Na.Application of Deep Learning in Mixed Reality Workshop Inspection.COMPUTER SYSTEMS APPLICATIONS,2022,31(5):118-123
