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红外成像和倾斜摄影三维融合在建筑检测中的应用

孙保燕 莫春华 薛伟 张玉涛 葛广昊

孙保燕, 莫春华, 薛伟, 张玉涛, 葛广昊. 红外成像和倾斜摄影三维融合在建筑检测中的应用[J]. 红外技术, 2022, 44(9): 991-998.
引用本文: 孙保燕, 莫春华, 薛伟, 张玉涛, 葛广昊. 红外成像和倾斜摄影三维融合在建筑检测中的应用[J]. 红外技术, 2022, 44(9): 991-998.
SUN Baoyan, MO Chunhua, XUE Wei, ZHANG Yutao, GE Guanghao. Application of 3D Fusion of Infrared Imaging and Tilt Photography in Building Detection[J]. Infrared Technology , 2022, 44(9): 991-998.
Citation: SUN Baoyan, MO Chunhua, XUE Wei, ZHANG Yutao, GE Guanghao. Application of 3D Fusion of Infrared Imaging and Tilt Photography in Building Detection[J]. Infrared Technology , 2022, 44(9): 991-998.

红外成像和倾斜摄影三维融合在建筑检测中的应用

基金项目: 

广西创新驱动发展专项资金项目 AA19182023

广西科学研究与技术开发计划项目 1598019-8

广西研究生教育创新计划项目 YCSW2020164

详细信息
    作者简介:

    孙保燕(1962-),男,本科,教授级高工,主要研究方向为建筑智能信息检测、数字建造与BIM应用。E-mail: sunbaoyan@126.com

  • 中图分类号: TN215

Application of 3D Fusion of Infrared Imaging and Tilt Photography in Building Detection

  • 摘要: 针对建筑病害缺乏有效和高精度无损检测手段,同时传统人工检测受建筑围护结构饰面层病害区域的高度、面积等方面的限制,提出一种红外成像和倾斜摄影三维融合并建立含有建筑病害信息的三维模型电子档案的方法。本文以学校某教学楼为例,以红外技术为主,倾斜摄影技术为辅的采集方案,采用同位空间坐标匹配法,经过坐标转换和2种异源空间数据融合,获取含有建筑病害信息的精细化三维模型电子档案,并完成数据融合前后的模型精度对比评估。结果表明:此方法得到的融合模型精度高,点位误差小,能快速、精准获取建筑病害空间位置,为建筑无损检测技术的实际应用提供新的思路,对建立建筑信息化监、修、管一体化运维体系具有研究价值和实际应用意义。
  • 图  1  红外检测工作原理

    Figure  1.  The working principle of infrared detection

    图  2  环绕航摄自动采集示意图

    Figure  2.  Oblique photography data collection method

    图  3  坐标系转换示意图

    Figure  3.  Schematic diagram of coordinate system conversion

    图  4  无损检测技术融合模型流程

    Figure  4.  Non-destructive testing technology fusion model process

    图  5  组合式环绕航摄采集影像

    Figure  5.  Combined surround aerial photography to collect images

    图  6  红外热像仪探测示意图

    Figure  6.  Schematic diagram of infrared camera detection

    图  7  带真实纹理的三维模型

    Figure  7.  3D model with real texture

    图  8  教学楼某一面围护结构饰面层的空鼓病害红外热图像及处理结果

    Figure  8.  Infrared thermal image and processing results of hollow drum disease on the surface layer of a certain envelope of the teaching building

    图  9  空鼓病害二维图像及灰度处理结果

    Figure  9.  Two-dimensional image of hollow drum disease and gray-scale processing results

    图  10  裂缝病害二维图像及灰度处理结果

    Figure  10.  Two-dimensional image of crack disease and gray-scale processing result

    图  11  空鼓病害区域标示图

    Figure  11.  Hollow disease area

    图  12  裂缝病害区域标示图

    Figure  12.  Marking map of marking map crack disease area

    图  13  融合模型精度误差统计分析图

    Figure  13.  The statistical analysis of the accuracy error of the fusion model

    表  1  异源数据融合精度结果

    Table  1.   Accuracy results of heterogeneous data fusion m

    Dot ΔX ΔY ΔZ ΔS
    S01 -0.014 0.009 -0.017 0.024
    S02 -0.013 -0.018 0.011 0.025
    S03 -0.020 -0.011 0 0.023
    S04 0.009 0 -0.010 0.013
    S05 -0.011 0.005 0.014 0.018
    S06 0.010 0.014 0.009 0.019
    S07 0 0 -0.010 0.010
    S08 0.013 0.010 0.011 0.020
    S09 0.010 0 0.013 0.016
    S10 0.013 0.010 0.010 0.019
    S11 0.006 -0.008 0.010 0.014
    S12 0.012 0.010 0 0.016
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-09
  • 修回日期:  2022-01-28
  • 刊出日期:  2022-09-20

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