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基于红外图像处理技术的建筑外窗缺陷面积计算研究

张玲玲 许廒 张继冉 任攀攀 丁立斌 魏代晓

张玲玲, 许廒, 张继冉, 任攀攀, 丁立斌, 魏代晓. 基于红外图像处理技术的建筑外窗缺陷面积计算研究[J]. 红外技术, 2022, 44(12): 1358-1366.
引用本文: 张玲玲, 许廒, 张继冉, 任攀攀, 丁立斌, 魏代晓. 基于红外图像处理技术的建筑外窗缺陷面积计算研究[J]. 红外技术, 2022, 44(12): 1358-1366.
ZHANG Lingling, XU Ao, ZHANG Jiran, REN Panpan, DING Libin, WEI Daixiao. Research on Calculation of Defect Area of Building Exterior Windows Based on Infrared Image Processing Technology[J]. Infrared Technology , 2022, 44(12): 1358-1366.
Citation: ZHANG Lingling, XU Ao, ZHANG Jiran, REN Panpan, DING Libin, WEI Daixiao. Research on Calculation of Defect Area of Building Exterior Windows Based on Infrared Image Processing Technology[J]. Infrared Technology , 2022, 44(12): 1358-1366.

基于红外图像处理技术的建筑外窗缺陷面积计算研究

详细信息
    作者简介:

    张玲玲(1972-),女,教授,研究方向:建筑节能与绿色建筑。E-mail: 305125954@qq.com

  • 中图分类号: TU111.4

Research on Calculation of Defect Area of Building Exterior Windows Based on Infrared Image Processing Technology

  • 摘要: 将红外热成像与图像处理技术结合应用于建筑外窗缺陷的检测,提出一种外窗缺陷检测和面积计算方法。通过外窗缺陷检测实验,利用压差法进行外窗空气渗透检测,求出渗透的缺陷面积。将红外热成像仪采集的外窗红外图像进行图像的预处理、外窗缺陷的检测以及检测后的面积计算,并建立外窗缺陷红外图像检测模型。结果表明:利用加权平均法进行灰度化处理,中值滤波进行降噪处理、图像锐化和直方图均衡化进行图像增强处理,处理效果明显,可作为外窗红外图像的预处理方式;Roberts算法对预处理后外窗红外图像的检测与实验值差异最小,检测信息更接近实际缺陷位置;将处理方法和检测模型与建筑整体气密性检测结合,能够在现场对外窗气密性能等级进行初步判定。
  • 图  1  红外热成像仪采集建筑外窗图像原理

    Figure  1.  Schematic diagram of infrared thermal imager collecting images of building windows

    1. Thermal imager; 2. Tripod; 3. Building wall; 4. Building windows

    图  2  像素面积法计算原理

    Figure  2.  Schematic diagram of pixel area calculation

    图  3  灰度化处理图(a)~(h)与灰度分布直方图(a1)~(h1)

    Figure  3.  Gray processing diagram(a)-(h) and gray distribution histogram(a1)-(h1)

    图  4  图像增强结果对比(a)、(f)、(k)灰度化处理;(b)、(g)、(l)中值滤波处理;(c)、(h)、(m)拉普拉斯锐化;(d)、(i)、(n)直方图均衡化;(e)、(j)、(o)两种方式混合

    Figure  4.  Comparison of image enhancement results: (a), (f), (k) Gray scale processing; (b), (g), (l) Median filtering; (c), (h), (m) Laplacian sharpening; (d), (i), (n) Histogram equalization; (e), (j), (o) Two methods mixed

    图  5  外窗红外图像预处理结果:(a)、(e)、(i)原始红外图像;(b)、(f)、(j)灰度化处理;(c)、(g)、(k)中值滤波;(d)、(h)、(l)图像增强结果

    Figure  5.  Preprocessing results of infrared image of windows: (a), (e), (i) Infrared imagery; (b), (f), (j) Gray scale processing; (c), (g), (k) Median filtering; (d), (h), (l)Image enhancement

    图  6  各方法进行外窗缺陷检测结果:(a)~(f) Roberts、Sobel、Prewitt、Canny、Log算法、阈值分割法

    Figure  6.  Testing results of window defects by each method: (a)-(f)Roberts, Sobel, Prewitt, Canny, Log, threshold segmentation

    图  7  各方法检测后形态学处理结果:(a)~(f)Roberts、Sobel、Prewitt、Canny、Log算法、阈值分割法

    Figure  7.  Morphological processing results after detection by each method: (a)-(f)Roberts, Sobel, Prewitt, Canny, Log, Threshold segmentation

    图  8  外窗缺陷实验值与图像处理值的散点图

    Figure  8.  Scatter diagram of experimental values and image processing values of window defects

    表  1  外窗缺陷面积对比

    Table  1.   Defect area comparison table of windows

    Windows Value of experiment/cm2 Roberts/cm2 Sobel/cm2 Prewitt/cm2 Canny/cm2 Log/cm2 Threshold value segmentation/cm2
    C0407(1) 0.90 0.96 1.39 1.39 1.70 1.62 0.92
    C0407(2) 1.50 1.50 1.80 1.80 1.40 2.10 1.50
    C0709(1) 3.20 3.28 4.07 3.94 3.49 5.86 3.68
    C0709(2) 3.20 3.21 4.03 3.84 3.46 4.48 3.65
    C0814(1) 4.30 5.19 11.32 11.07 6.58 11.61 4.70
    C0814(2) 4.10 4.53 11.93 11.81 5.59 12.64 4.30
    C1218(1) 11.20 11.50 14.44 14.40 15.44 7.20 11.52
    C1218(2) 10.50 10.95 18.13 17.89 11.73 14.47 11.32
    C1218(3) 10.30 11.03 14.22 14.02 10.89 14.76 10.43
    C1218(4) 11.10 11.30 14.07 13.82 12.94 7.93 11.21
    C1716(1) 9.20 9.30 11.92 11.90 9.32 4.96 9.30
    C1716(2) 14.50 15.01 16.79 16.62 14.83 9.57 14.24
    C2114(1) 15.80 14.86 25.42 24.88 22.93 23.33 16.87
    C2114(2) 11.50 13.80 17.20 17.00 13.80 16.30 13.90
    C2418(1) 22.10 24.50 30.92 30.50 36.28 26.65 25.66
    C2418(2) 20.90 24.60 31.80 31.40 23.40 26.90 24.60
    Error of mean - 7.23% 56.01% 53.76% 26.18% 61.60% 7.67%
    下载: 导出CSV
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  • 收稿日期:  2021-11-16
  • 修回日期:  2021-12-27
  • 刊出日期:  2022-12-20

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