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

基于红外图像处理技术的建筑外窗缺陷面积计算研究

1.
烟台大学建筑学院, 山东烟台 264005
2.
中铁建工集团第二建设有限公司, 山东青岛 266112
3.
烟台市建筑设计研究股份有限公司, 山东烟台 264005

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
计量
- 文章访问数: 20
- HTML全文浏览量: 11
- PDF下载量: 8
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-16
- 修回日期: 2021-12-27
- 刊出日期: 2022-12-20

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

1.
School of Architecture, Yantai University, Yantai 264005, China
2.
China Railway Construction Engineering Second Construction Co. LTD., Qingdao 266112, China
3.
Yantai Architectural Design and Research Co., Ltd., Yantai 264005, China

摘要

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。
- 外窗缺陷 /
- 面积计算 /
- 红外热成像 /
- 图像处理
Abstract: A method for defect detection and area calculation of exterior windows of buildings is proposed by combining infrared thermal imaging technology and image processing technology. Using equipment for detection of building exterior window defects, the differential-pressure method was utilized to detect the air penetration of an exterior window, and the defective area of the air penetration of this window was calculated. Infrared images of the exterior window of the building collected by an infrared thermal imager were subjected to image preprocessing, exterior window defect detection, and area calculation after inspection. Then, an infrared-image detection model of exterior window defects was established. The results show that preprocessing can make use of the weighted average method for grayscale processing, the median filter for noise reduction, image sharpening, and histogram equalization for image enhancement processing. The outcome of the aforementioned approaches is evident. The detection of the pretreatment infrared image, which is obtained using the Roberts algorithm, minimizes the difference between the test and experimental values. This makes the detection information closer to the actual position of the defect. A primary assessment of the airtightness performance level of exterior windows can be achieved by comparing the results provided by the proposed infrared image processing technology with airtightness on-site tests.
- Exterior window defects /
- area calculation /
- infrared thermal imaging /
- image processing

HTML全文

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

下载: 全尺寸图片幻灯片

图 2 像素面积法计算原理

Figure 2. Schematic diagram of pixel area calculation

下载: 全尺寸图片幻灯片

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)

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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%

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