On-Site Detection of Airtightness of Building Windows Based on Infrared Image Processing
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摘要: 针对目前建筑外窗气密性现场检测方法无法保证其所有安装外窗的气密性等级全部达标，同时缺乏高效、便捷的检测手段，提出一种外窗气密性能等级现场检测方法，通过热像仪对外窗进行红外图像采集，对图像中的异常区域进行缺陷检测并计算缺陷面积，建立外窗缺陷红外检测模型。根据实验测得的室内外温差、外窗缺陷面积、空气渗透量，建立外窗空气渗透量计算模型，将此模型与外窗缺陷红外检测模型结合求得窗户的空气渗透量，实现外窗气密性能的现场检测，初步判定外窗是否符合相应的气密性能等级，提高了外窗气密性能现场检测的效率，为外窗气密性能等级现场判定提供一种新的途径。Abstract: Current methods of on-site detection of airtightness of building windows cannot ensure that the airtightness grades of all windows satisfy the standard. Moreover, there is a lack of efficient and convenient detection methods. Thus, we proposed an on-site method to detect the airtightness performance level of windows. In this study, an infrared image of the windows is collected using a thermal imager, the abnormal area in the image is detected and the defect area is calculated, then an infrared detection model for window defects is established. Based on the experimentally measured indoor–outdoor temperature difference, the defect area of the window and air infiltration, a calculation model for the air infiltration of windows is built. The model is combined with the infrared detection model of exterior windows defects to obtain the air infiltration of the window, and the on-site detection of the windows airtightness performance is realized and then preliminary determine of whether the window meets the corresponding airtightness performance level, which improves the efficiency of the on-site inspection of the airtightness performance of windows and provides a new method for the on-site determination of the airtightness performance level of windows.
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图 2 各方法进行外窗缺陷检测结果
Figure 2. Detect results of the defects of windows by various methods
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图 3 外窗缺陷实验值与图像处理值的散点图
Figure 3. Scatter plot of the experimental value and the image processing value of the window defect
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图 4 外窗空气渗透量计算模型神经网络回归分析
Figure 4. Regression analysis diagram of calculating model of air infiltration amount of windows with neural network
表 1 外窗缺陷面积对比
Table 1 Defect area comparison 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 3.21 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 C1218（1） 11.20 11.50 14.44 14.40 15.44 7.20 11.52 C1218（2） 10.30 11.03 14.22 14.02 10.89 14.76 10.43 C1716（1） 9.20 9.30 11.92 11.90 9.32 4.96 9.30 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） 20.90 24.60 31.80 31.40 23.40 26.90 24.60 
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表 2 各处理方式误差汇总
Table 2 Summary of errors of each processing method
Roberts Sobel Prewitt Canny Log Threshold segmentation 7.36% 56.21% 53.86% 26.35% 61.92% 7.97%
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表 3 外窗气密性能等级现场判定结果对比
Table 3 Comparison of site judgment results of airtightness performance grade of windows
Windows Experimental result Model result Relative error Air permeability/(m3/h) Air permeability per unit area/(m3/(m2·h)) Air permeability/(m3/h) Air permeability per unit area/(m3/(m2·h)) C0923 7.00 3.38 7.32 3.54 4.60% C0918 6.00 3.70 5.76 3.55 3.94% C1823 17.00 4.11 18.28 4.41 7.41% C0910 4.00 4.44 3.93 4.36 1.69% C0623 5.00 3.62 5.13 3.72 2.69% C0924 7.00 3.24 7.11 3.29 1.62% C1523 12.00 3.48 13.06 3.78 8.76% C1824 14.00 3.24 15.08 3.49 7.77% C1822 17.00 4.29 17.74 4.48 4.41% C1514 8.00 3.81 8.10 3.86 1.22%
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