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1. 清华大学机械工程系,北京100084
2. 清华大学基础工业训练中心,北京 100084
3. 清华大学深圳国际研究生院先进制造学部,广东 深圳 518055;4 航天东方红卫星有限公司,北京 100094
[ "赵甘霖(1995- ),男,清华大学机械工程系博士生,主要研究方向为机器视觉、增强现实辅助装配技术等" ]
[ "余畅(1998- ),男,清华大学机械工程系博士生,主要研究方向为机器视觉、增强现实、智能制造等" ]
[ "张建富(1975- ),男,博士,清华大学机械工程系副教授、特别研究员,主要研究方向为精密加工技术、超声加工技术及系统、先进制造装备、智能制造系统等" ]
[ "杨建新(1977- ),男,博士,清华大学基础工业训练中心副教授,主要研究方向为增强现实、智能制造、精密测量等" ]
[ "冯平法(1966- ),男,博士,清华大学机械工程系教授,主要研究方向为智能制造与精密加工等" ]
[ "沈群(1989- ),男,航天东方红卫星有限公司工程师,主要研究方向为卫星制造智能装备等" ]
纸质出版日期:2021-09-30,
网络出版日期:2021-09,
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赵甘霖, 余畅, 张建富, 等. 基于AR虚实图像注意力机制的电缆装配质量检测方法[J]. 物联网学报, 2021,5(3):27-38.
GANLIN ZHAO, CHANG YU, JIANFU ZHANG, et al. Inspection method for cable assembly quality based on AR virtual-real image attention mechanism. [J]. Chinese journal on internet of things, 2021, 5(3): 27-38.
赵甘霖, 余畅, 张建富, 等. 基于AR虚实图像注意力机制的电缆装配质量检测方法[J]. 物联网学报, 2021,5(3):27-38. DOI: 10.11959/j.issn.2096-3750.2021.00236.
GANLIN ZHAO, CHANG YU, JIANFU ZHANG, et al. Inspection method for cable assembly quality based on AR virtual-real image attention mechanism. [J]. Chinese journal on internet of things, 2021, 5(3): 27-38. DOI: 10.11959/j.issn.2096-3750.2021.00236.
航天器中电缆装配在总装环节占据大量比重,电缆装配质量直接影响着产品整机的工作性能。现有的增强现实辅助电缆装配系统缺乏对装配结果实时判断和反馈的能力,无法识别当前装配的电缆是否符合装配质量要求。为了解决上述问题,提出了一种基于AR虚实图像注意力机制的电缆装配质量检测方法,通过构建虚拟辅助模型,在拍摄的装配结果图像中定位检测关键区域,过滤冗余图像信息;进而基于 YOLOv4-Tiny 图像检测模型对电缆装配关键节点位置进行了判断,基于计算电缆邻域平均重合度的方法求得了敷设路径的重合度,基于相机逆投影的方法得到了电缆的弯曲半径。最后,利用HoloLens2开发了基于增强现实技术的电缆装配质量检测系统,并对设计的4个电缆装配案例进行了质量检测,实验验证了本文方法及系统的可行性和有效性。
Cable assembly in spacecraft occupies a large proportion in the final assembly process
and the quality of cable assembly directly affects the working performance of the whole product.The existing augmented reality-assisted cable assembly system lacks the ability to make real-time judgment and feedback on the assembly results
and cannot identify whether the currently assembled cable meets the quality requirements.In order to solve the above problems
a cable assembly quality inspection method based on AR virtual-real image attention mechanism was proposed.Critical areas in the captured images were located and detected by constructing virtual assist models to filter redundant image information.Based on YOLOv4-Tiny image detection model
the key node location of assembled cable was judged.The overlap of the laying path was obtained based on the method of calculating the average overlap of the neighborhood
and the bending radius of the cable was derived based on the method of camera inverse projection.Finally
an augmented reality-based cable assembly quality inspection system was developed using HoloLens2.The quality inspection was performed on the four designed cable assembly cases
and the feasibility and effectiveness of the method and system were experimentally verified.
电缆装配质量检测增强现实辅助装配AR虚实图像注意力机制
cable assemblyquality inspectionaugmented reality assisted assemblyAR virtual-real imageattention mechanism
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