WO2021248915A1 - Procédé et système d'analyse/de détection de différence de couleur pour béton de parement - Google Patents

Procédé et système d'analyse/de détection de différence de couleur pour béton de parement Download PDF

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WO2021248915A1
WO2021248915A1 PCT/CN2021/073124 CN2021073124W WO2021248915A1 WO 2021248915 A1 WO2021248915 A1 WO 2021248915A1 CN 2021073124 W CN2021073124 W CN 2021073124W WO 2021248915 A1 WO2021248915 A1 WO 2021248915A1
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color
image
fair
area
color difference
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PCT/CN2021/073124
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Chinese (zh)
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范宏
郭思瑶
张鹏
金祖权
万小梅
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青岛理工大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30132—Masonry; Concrete

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  • the invention belongs to the field of civil engineering fair-faced concrete appearance quality evaluation, and specifically relates to a color difference evaluation method for color images.
  • Fair-faced concrete is the concrete that is formed at one time without any decoration. It uses the natural texture of the concrete itself and the natural state formed by the combination of carefully designed open seams, zen seams and tension bolt holes as the architectural expression of the decorative surface. It is widely used In industrial buildings, civil engineering high-rise buildings, public buildings and municipal bridges.
  • Fair-faced concrete construction originated from abroad and has been widely used in Japan, Europe and the United States and other countries, becoming a new architectural genre. my country's fair-faced concrete technology began to be tried in the late 1980s, and its development process can be summarized in four stages: original fair-faced concrete, fair-faced concrete, mirror-finished fair-faced concrete and colored fair-faced concrete.
  • fair-faced concrete is uniform, smooth, and beautiful in color, without any modification, with accurate cross-sectional dimensions, rounded edges and corners, smooth lines, and natural transition between layers.
  • the visual quality of fair-faced concrete has reached a higher artistic level, without any decoration, and the plaster layer and surface layer are eliminated.
  • Significant environmental benefits fair-faced concrete technology eliminates plastering and wet work, improves the level of civilized construction on site, reduces winter construction, and also reduces construction waste.
  • the fair-faced concrete technology eliminates the plastering layer, the common quality problems of plastering engineering that are easy to hollow, fall off and cracks are eliminated.
  • Fair-faced concrete requires that the surface of the concrete is smooth and smooth, with uniform color and no damage or pollution.
  • the setting of the tension bolts and construction joints should be neat and beautiful, and the common quality defects of ordinary concrete are not allowed. But in fact, due to the complex construction process of fair-faced concrete, the level of the construction team is different, and there are no strict quality acceptance specifications and technical standards to follow in China. It is very easy to cause defects in the appearance of fair-faced concrete, among which color defects are the most common. Therefore, the evaluation of chromatic aberration is of great significance to the construction of fair-faced concrete. There are two commonly used methods for evaluating the color difference of fair-faced concrete:
  • the manual evaluation method is the method adopted in my country's standard fair-faced concrete application technical regulations (JGJ 169-2009), and is currently the most widely used method.
  • the specific method is: randomly select three to five trained color difference inspectors, stand at a designated distance, and score and evaluate the selected concrete surface area. Finally, the average score of all color difference inspectors is taken to evaluate the color difference defects.
  • Artificial evaluation methods are more susceptible to the influence of people's subjective consciousness, resulting in evaluation results that are not objective and accurate.
  • Gray-scale image evaluation method Z. Zhu (Zhu Z, Brilakis I. Detecting air pockets for architectural quality assessment using visual sensing[J]. Electronic Journal of Information Technology in Construction, 2008, 13: 86-102.) proposed a gray-scale image based In the color difference evaluation method, the specific steps are to obtain a concrete image, perform gray-scale conversion on the image, and calculate the standard deviation of the gray-scale image. Then the chromatic aberration defects are simply classified as qualified and unqualified. Peng Haitao (Peng Haitao, Su Jie, Fang Zhi, et al. Detection and evaluation of concrete surface chromatic aberration based on image analysis technology[J]. Highway Engineering, 2012(5), 19-22) On the basis of Z. Zhu, it is also calculated image The standard deviation of gray scale, but introduces the concepts of human eye brightness contrast threshold, viewing angle size, etc., and fuzzy evaluation of color difference defects based on the graded membership function.
  • the above gray-scale image evaluation methods are all based on the gray-scale image method, and the color difference is evaluated based on the standard deviation. Converting an RGB image into a grayscale image according to a certain algorithm, but the conversion into a grayscale image has a major disadvantage. After the conversion, the color information of the image will be lost to a greater extent, resulting in inaccurate evaluation.
  • RGB color mode is a color standard in the industry. It obtains a variety of colors by changing the three color channels of red (R), green (G), and blue (B) and superimposing them with each other. Yes, RGB is the color representing the three channels of red, green, and blue. This standard includes almost all the colors that human vision can perceive, and it is currently one of the most widely used color systems.
  • Each of the three color channels of red, green and blue is divided into 255 levels of brightness. At 0, the "light” is the weakest and turned off, and at 255, the "light” is the brightest. When the three color values are the same, it is a colorless grayscale color, and when the three colors are all 255, it is the brightest white, and when the three colors are all 0, it is black.
  • the three color channel values corresponding to each typical color are shown in Table 1.
  • RGB images can be converted into grayscale images. There are many ways to convert RGB values and grayscales. In fact, it is the conversion of the human eye's perception of color to brightness. This is a psychological problem. The commonly used conversion has the following formula:
  • the conversion of gray-scale images is based on the visual sense of the human eye, for different color components, taking different weights, the R component is 0.229, the G component is 0.587, and the B component is 0.114. Then add up to get the gray value.
  • the three color components are R:100G:0B:0 (brown), R:0G:40B:0 (blue), and R:0G:0B:200 (dark green).
  • their gray scale values are all approximately equal to 23. It can be seen that the grayscale conversion of different color images loses image information and cannot reflect the human eye's perception of color differences.
  • the present invention proposes a new method of color difference evaluation method based on RGB color image, which converts the RGB color image into RGB three color channel values, calculates the standard deviation for each color channel, and then The standard deviation of the three color channels is analyzed to realize the accurate evaluation of the color difference analysis of fair-faced concrete.
  • the relative area the ratio of the color difference area to the entire image
  • the degree of difference the size of the color difference between the color difference defect area and the non-color difference defect area
  • Image segmentation is used to determine the relative area
  • the color difference formula is used to determine the degree of difference.
  • the color image-based chromatic aberration defect evaluation method avoids the subjectivity of artificial chromatic aberration defect evaluation and avoids the loss of image information based on gray-scale image chromatic aberration defect evaluation. The specific process is shown in Figure 1.
  • the first aspect of the present invention provides a method for analyzing the color difference of fair-faced concrete, including:
  • the present invention converts the RGB color image into RGB three color channel values, calculates the standard deviation of each color channel, and then analyzes the standard deviation of the three color channels to realize the accurate evaluation of the color difference analysis of fair-faced concrete.
  • the second aspect of the present invention provides a fair-faced concrete color difference analysis system, including:
  • a module for evaluating the color consistency of concrete appearance by using the gray standard deviation value is a module for evaluating the color consistency of concrete appearance by using the gray standard deviation value.
  • the third aspect of the present invention provides a method for detecting the color difference of fair-faced concrete, including:
  • Image segmentation is performed by combining region merging and quadtree segmentation, and the chromatic aberration area and the non-chromatic aberration area are segmented;
  • the chromatic aberration area is rated with reference to the quality evaluation table.
  • the present invention uses image segmentation to determine the relative area, and uses a color difference formula to determine the degree of difference.
  • the color image-based chromatic aberration defect evaluation method avoids the subjectivity of artificial chromatic aberration defect evaluation, and avoids the loss of image information based on gray-scale image chromatic aberration defect evaluation, and the evaluation result is more accurate.
  • the fourth aspect of the present invention provides a system for detecting the color difference of fair-faced concrete, which includes:
  • a module for grading the color difference area A module for grading the color difference area.
  • the present invention converts the RGB color image into three RGB color channel values, calculates the standard deviation of each color channel, and then analyzes the standard deviation of the three color channels to realize the evaluation of the color difference analysis of fair-faced concrete , The evaluation result is clearer and more accurate.
  • the present invention uses image segmentation to determine the relative area, and uses a color difference formula to determine the degree of difference.
  • the color image-based chromatic aberration defect evaluation method avoids the subjectivity of artificial chromatic aberration defect evaluation, and avoids the loss of image information based on gray-scale image chromatic aberration defect evaluation.
  • FIG. 1 is a flowchart of the present invention
  • Figure 2 is a standard image taken in Embodiment 1;
  • Fig. 3 is a diagram showing the decomposition of the image into three color channels in embodiment 1;
  • Embodiment 4 is a distribution diagram of pixel values of three color channels in Embodiment 1;
  • Figure 5 is a standard image taken in Embodiment 2.
  • FIG. 6 is a diagram of the image decomposed into three color channels in Embodiment 2;
  • FIG. 7 is a distribution diagram of pixel values of three color channels in Embodiment 2.
  • FIG. 8 is a diagram of the quadtree segmentation of Embodiment 3.
  • Figure 9 is a color difference defect diagram of Example 3.
  • Fig. 10 is a Lab color space diagram of embodiment 3.
  • FIG. 11 is a color difference defect segmentation diagram of Example 3.
  • the chromatic aberration area can be regarded as the target object with the normal concrete surface as the background. Due to the intricacies of environmental conditions, shooting conditions (such as environmental brightness, camera resolution, and shooting distance, etc.) have an important influence on the results of image analysis. In order to avoid the influence of environmental brightness, camera resolution and shooting distance on the standard deviation of image gray levels, to obtain standard images, the collected images should meet the following conditions:
  • the photographing distance should be 4 to 6 meters.
  • the length of the photograph corresponding to the size of the subject is 0.9 to 1.1 meters and the width is 0.5 to 0.7 meters.
  • Achieve white balance that is, "the white object can be restored to white regardless of the light source.”
  • AFB automatic white balance
  • most digital SLR cameras support custom white balance, and in the custom white balance operation, it is a better practice to use a standard gray card.
  • the method to achieve white balance is as follows:
  • Standard gray card Use the white surface, enter the "custom white balance mode" to take a picture of the white surface of the standard gray card, and DC will know what is white under the light conditions.
  • RGB images are also called full-color images. There are three channels: R (red), G (green), and B (blue). Use image software to separate the standard RGB image into three channels, and obtain the three-channel gray value. After separation, three matrices representing R (red), G (green), and B (blue) are obtained, and the values of the matrices are distributed in the range of 0 to 255.
  • M and N respectively represent the number of rows and columns of the color channel image
  • Gray (i, j) represents the gray value of each pixel
  • Gray represents the average gray value of the entire image.
  • the gray standard deviation value is used as the evaluation standard for the consistency of concrete appearance color, as shown in Table 2. Take the maximum value of the standard deviations of the three color channels, and evaluate the gray-scale standard deviation less than 6 as the first-class appearance color consistency, the gray-scale standard deviation less than 10 as qualified, and the gray-scale standard deviation greater than 10. In order to fail to meet the requirements for the appearance and color consistency of fair-faced concrete, it is unqualified.
  • the present invention proposes a method for evaluating the color difference of fair-faced concrete, including:
  • the color space of the RGB image is converted into a color space Lab space which is closer to the color recognition mechanism of the human eye.
  • the RGB color space is the most commonly used color space. Electronic devices such as digital cameras and scanners all use the RGB color space to represent colors. Therefore, the RGB color space is also referred to as the device-related color space.
  • Lab was established on the basis of the international standard for color measurement established by the International Commission on Illumination (CIE) in 1931. It is a device-independent color system as well as a color system based on physiological characteristics. This also means that it uses digital methods to describe human visual perception.
  • the Lab color gamut is wide, not only includes all the color gamuts of RGB, but also expresses the colors they cannot express. The colors that the human eye can perceive can be expressed through the Lab model, which makes up for the lack of uneven RGB color distribution, and In the Lab color space, the space coordinate distance between two points can be used to express the color difference ⁇ .
  • XYZ color space is required as a transition.
  • the XYZ color space is also a color space introduced by the International Commission on Illumination (CIE) on the basis of RGB.
  • CIE International Commission on Illumination
  • a new color space is established with three imaginary primary colors X, Y, and Z.
  • X n , Y n , and Z n are the tristimulus values of the CIE standard illuminator on the complete diffuse reflector, and then through the complete diffuse reflector to the observer's eye.
  • the values are usually 95, 100, 108, respectively.
  • Image segmentation can better identify the chromatic aberration defects on the surface of the concrete, and lay the foundation for the subsequent evaluation of chromatic aberration defects.
  • the segmentation based on the color image ensures the completeness of the information of the concrete surface image.
  • the histogram threshold method is a widely used segmentation method for grayscale images, but it will appear when it is applied to a color image, and the region obtained by segmentation may be incomplete; the histogram of a color image is a three-dimensional array and There may not necessarily be obvious valleys, which are used for thresholding segmentation; there are no problems such as using local spatial information.
  • Edge detection is also a widely used technique for grayscale image segmentation.
  • the present invention systematically analyzes and researches the existing graphics segmentation method. Aiming at the characteristics of fair-faced concrete surface smoothness, high color saturation, and good overall uniformity, it proposes the use of region merging and quadtree segmentation.
  • the combined image segmentation method effectively identifies the chromatic aberration defect area on the surface of fair-faced concrete, ensures the information integrity of the concrete surface image, and has high accuracy in the evaluation of chromatic aberration defects.
  • Region merging is to classify images according to certain characteristics, different categories are classified into different sets, and the same categories are classified into the same set. Combine the pixels of the same set to make them into a whole.
  • Quadtree segmentation is to divide the image into four rectangular regions of the same size, and set the threshold.
  • the rectangular area that meets the threshold requirement is no longer segmented; the rectangular area that does not meet the threshold requirement, the quadtree segmentation continues. Repeat this way, until the segmented area is a single pixel or when the segmented area meets the threshold requirement, the quadtree segmentation is stopped, as shown in FIG. 8.
  • the specific steps are: 1. Perform sub-block segmentation on the image. 2. Perform area merging on the divided pure color sub-blocks. 3. Perform quadtree division on the non-pure color sub-blocks, and then merge the regions. 4. Perform region merging on the regions divided by the pure color sub-block and the non-pure color sub-block.
  • the image is divided into sub-blocks, and the image is divided into m ⁇ n sub-blocks. It should be noted that the values of m and n can be adjusted according to the actual size of the image.
  • x ij represents a vector composed of the L, a, and b values of the pixel in the i-th row and the j-th column in a sub-block of pixels in the c row and the d column.
  • the variance vector is:
  • the pure-color sub-block has a small variance because the inner color is uniform, and the non-pure-color sub-block has a larger variance because it contains a color difference area. Thresholds can be set according to the situation to distinguish between pure-color sub-blocks and non-pure-color sub-blocks.
  • the pure color sub-block is divided into two parts, one part is the color difference area, and the other part is the non-color difference area.
  • the non-pure color sub-blocks are divided by a quadtree method.
  • the non-pure color sub-block is divided into four small sub-blocks of the same size and shape.
  • step (3) Repeat the above step (2) until all sub-blocks are pure color sub-blocks or one pixel. End the quadtree split.
  • the first uniform block or uniform pixel in the quadtree segmentation process is taken as the set P 1 .
  • the non-pure color sub-block is also divided into two parts, the color difference area and the non-color difference area.
  • Color difference is the description of people's perception of different colors.
  • the space coordinate distance between two points is used to express the color difference ⁇ , as shown in formulas (9) to (12).
  • NBS color difference unit 1
  • the human eye recognition result is basically no color difference; 3-6
  • NBS there is a big difference; when there are more than 12 NBS, it will be recognized as different colors by the human eye. Details are shown in Table 3.
  • the degree of difference is expressed as the degree of color difference between the chromatic aberration defect area and the entire concrete shooting surface. The greater the color difference, the serious color difference on the surface of the concrete.
  • the color distance ⁇ is used to indicate the degree of difference.
  • B Indicates the size of the chromatic aberration defect area relative to the entire image area.
  • B is used to represent the relative area
  • S O represents the area of the chromatic aberration defect area
  • S represents the area of the entire concrete shooting surface. Note that S O and S here are in pixels. Then B is:
  • the concrete color difference area is graded and evaluated.
  • the evaluation form is established here in accordance with the "Code for Acceptance of Construction Quality of Concrete Structure Engineering" (GB50204-2015).
  • the table is divided into five levels, I, II, III, IV, and V. The larger the level, the more serious the chromatic aberration defect, as shown in Table 4.
  • the specific usage method is to evaluate the color difference defect based on the relative area and the degree of difference. When the two indicators belong to different levels, the higher level is used as the evaluation result.
  • Figure 3 Decomposes the image into three color channels
  • Formula (2) is used to calculate the standard deviation S of the three color channels, and the calculation results are shown in Table 5.
  • the gray standard deviation value is used as the evaluation standard of the color consistency of the concrete appearance.
  • the standard deviations of the three color channels are the red channel: 41.58, the blue channel: 66.07, the green channel: 50.96, and the maximum of the three is 66.07 , Compare the maximum value of 66.07 with the color difference judgment standard in Table 2.
  • the gray scale standard deviation is greater than 10, and it is judged as unqualified.
  • the fair-faced concrete image in a subway station is used for analysis.
  • the exposed concrete room is illuminated by light with sufficient light.
  • the photographing distance should be 5 meters, and the photographed fair-faced concrete should be 1 meter long and 0.65 meters wide.
  • white balance is realized, and standard images of fair-faced concrete are taken. As shown in Figure 5.
  • Formula (2) is used to calculate the standard deviation S of the three color channels, and the calculation results are shown in Table 6.
  • the gray standard deviation value is used as the evaluation standard of the color consistency of the concrete appearance.
  • the standard deviations of the three color channels are the red channel: 11.55, the blue channel: 15.51, the green channel: 14.31, and the maximum of the three is 15.51 , Compare the maximum value of 15.51 with the color difference judgment standard in Table 2.
  • the gray scale standard deviation is greater than 10, and it is judged as unqualified.
  • the bridge piers are poured with concrete, due to inadequate maintenance. Some areas on the piers have begun to produce chromatic aberration defects. As shown in Figure 9.
  • Figure 9(a) is rust. Because the upper metal members of the pillars are rusted, the lower pillars have chromatic aberration defects;
  • Figure 9(b) is the stain, which is caused by insufficient protection of the concrete surface.
  • Figure 9(c) shows the oil stains, due to improper use of the release agent when the concrete is being formed, resulting in chromatic aberration defects.
  • the method mentioned in the present invention is used to evaluate the chromatic aberration of the image.
  • the steps are:
  • Step 1 Take a picture of the area that needs to be evaluated for chromatic aberration, and it is required to ensure that the lens is parallel to the shooting surface as much as possible.
  • Step 2 Use formulas (3) ⁇ (6) to convert the RGB color space of the image to Lab color space as shown in Figure 10.
  • Step 3 Perform image segmentation on the color difference defect area of the image, and identify the color difference defect area as shown in Figure 11.
  • Step 4 Find the relative area of one of the evaluation indicators.
  • Step 5 Calculate the degree of difference between the color difference defect area and the non-color difference defect area.
  • the specific method is to use formulas (7) to (8) to find the color average vector of the color difference area and the non-color difference area. Then use equations (9) to (12) to find the color distance ⁇ , and use ⁇ to judge the degree of difference.
  • Step 6 Refer to Table 3 to evaluate the chromatic aberration defect area. The results are shown in Table 7.

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Abstract

L'invention concerne un procédé et un système d'analyse/de détection de différence de couleur pour béton de parement. Le procédé d'analyse consiste : à acquérir une image standard d'une surface en béton à détecter ; à convertir l'image standard en image RVB, et à effectuer une séparation en trois canaux chromatiques pour obtenir des valeurs de niveaux de gris des trois canaux chromatiques ; à calculer respectivement des écarts-type des trois canaux chromatiques pour obtenir les écarts-type des trois canaux chromatiques ; et à prendre la valeur maximale des écarts-types des trois canaux chromatiques en tant qu'écart-type de niveaux de gris, celui dont l'écart-type de niveaux de gris est inférieur à 6 étant évalué comme présentant une régularité chromatique d'aspect de première classe, celui dont l'écart-type de niveaux de gris est inférieur à 10 étant évalué comme devant être qualifié, et celui dont l'écart-type de niveaux de gris est supérieur à 10 étant évalué comme étant non qualifié. Le procédé évite la subjectivité qu'implique l'évaluation de défaut de différence de couleur artificielle et de perte d'informations d'image d'évaluation de défaut de différence de couleur reposant sur une image en niveaux de gris.
PCT/CN2021/073124 2020-06-10 2021-01-21 Procédé et système d'analyse/de détection de différence de couleur pour béton de parement WO2021248915A1 (fr)

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