CN114677340B - Concrete surface roughness detection method based on image edge - Google Patents
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- 230000003746 surface roughness Effects 0.000 title claims abstract description 20
- 238000001514 detection method Methods 0.000 title abstract description 13
- 238000003708 edge detection Methods 0.000 claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 238000005286 illumination Methods 0.000 claims description 27
- 238000001914 filtration Methods 0.000 claims description 9
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- 238000004049 embossing Methods 0.000 description 1
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- 238000003672 processing method Methods 0.000 description 1
- 238000007788 roughening Methods 0.000 description 1
- 238000007528 sand casting Methods 0.000 description 1
- 238000005488 sandblasting Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
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- 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
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- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
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- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/181—Segmentation; Edge detection involving edge growing; involving edge linking
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
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- 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
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- G06T2207/30132—Masonry; Concrete
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Abstract
The invention discloses a method for detecting concrete surface roughness based on an image edge; the detection method comprises the following steps: 1) Image acquisition is carried out on the surface of a sample block with known concrete roughness value; 2) Preprocessing the acquired image; 3) Graying the preprocessed color image; 4) Processing the gray level image; 5) Performing pixel-level and sub-pixel-level edge detection on the processed gray level image; 6) Performing logical OR operation on the edge image; 7) Calculating edge frequency; 8) Fitting edge frequency and roughness curves by a least square method; and (3) repeating the steps 1) to 7) for the concrete image with unknown roughness, and calculating the edge frequency to obtain the roughness value. The invention can detect the concrete surface roughness under portable mobile phone equipment, is simple and efficient, and achieves higher precision.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a method for detecting concrete surface roughness based on an image edge.
Background
Concrete of different ages is poured to create, repair or reform a concrete structure, and adequate bonding between concrete castings is required. The bond strength between the different castings is due to surface roughness. The surface roughness can be achieved in a variety of ways, such as sandblasting, roughening, embossing, and the like. At present, common detection methods for concrete surface roughness include a sand casting method, a mechanical probe method, a contour texture measuring method and the like. In the face of thousands of concrete blocks to be tested, the traditional method for detecting the concrete surface roughness is quite time-consuming, is complex to operate, is not easy to carry by operating equipment, and is expensive in detection cost.
Disclosure of Invention
The invention aims to solve the technical problem of providing a concrete surface roughness detection method based on an image edge, which can simply and efficiently detect the concrete surface roughness under portable mobile phone equipment and achieve higher precision.
The invention adopts the following technical scheme:
the method for detecting the concrete surface roughness based on the image edge comprises the following steps:
Step1: image acquisition
Image acquisition is carried out on the surface of a sample block with known concrete roughness value, and the roughness value is detected by a three-dimensional laser scanner;
Step 2: color image preprocessing
Preprocessing a color image, and correcting the problems of uneven brightness and unclear image caused by uneven illumination of the image;
Step 3: graying of color images
For the preprocessed color image, converting the RGB value of the pixel into a gray value by calculating the weighted sum of each pixel R, G and the B component to obtain a gray image;
step 4: gray scale image processing
The gray level image is subjected to mean value filtering to obtain a filtered image, and the effect of smoothing noise is achieved by using the mean value of pixels around a certain pixel point; secondly, carrying out saturation processing on the lowest 1% and the highest 1% of all pixel values, thereby improving the contrast of an output image;
Step 5: pixel-level and sub-pixel-level edge detection
Respectively adopting Canny operators to realize pixel-level edge detection on the gray level image processed in the step 4, and adopting Zernike moments to realize sub-pixel-level edge detection to respectively obtain pixel-level and sub-pixel-level edge images;
step 6: edge image logical OR operation
Performing logical OR operation on the edge image detected by the Canny edge detection and the subpixel level Zernike edge detection;
step 7: calculating edge frequency
Defining the ratio of the number of white edge pixels to the total pixels in the new image after logical OR operation as edge frequency, and assuming that the resolution of the image is X X Y, the edge frequency EF is:
Wherein N (1) is a white pixel and N (0) is a black pixel;
Step 8: fitting edge frequency and roughness curves using least squares
Fitting an edge frequency and a roughness curve by using a least square method based on the roughness data of all acquired concrete images with known surface roughness and the calculated corresponding edge frequency data;
further, for the concrete image with unknown roughness, only the steps 2 to 7 are repeated, the edge frequency is calculated, and then the corresponding roughness value is obtained based on the curve.
In the invention, in the step 2, the method for preprocessing the color image is as follows:
firstly, extracting illumination variable through bright-pass bilateral filtering, converting an RGB three-channel color image into an HSV channel, and estimating illumination component from the V channel;
and then constructing a two-dimensional Gama gamma function, adjusting parameters of the two-dimensional Gama function by utilizing the illumination distribution characteristic, reducing the brightness value of the area with over-illumination, improving the brightness value of the image of the area with over-illumination, and realizing the self-adaptive correction of the image with uneven illumination.
Compared with the prior art, the invention has the beneficial effects that:
The invention can adaptively correct the image with uneven illumination by an image processing method under the portable mobile phone equipment, obtains the edge of a more complete image by utilizing two-stage edge detection and performing logical OR operation, and proposes a new edge frequency corresponding to roughness to perform simple and efficient detection. More unknown concrete surface roughness is predicted by a concrete surface image of limited known roughness and has higher detection accuracy.
Drawings
FIG. 1 is an image taken of the raw concrete roughness surface of the present invention.
Fig. 2 is an image of an original color image preprocessed by the present invention.
FIG. 3 is an image of the invention after edge detection using Canny.
Fig. 4 is an image of the invention after edge detection using Zernike moments.
Fig. 5 is an image of the present invention after logical or-ing Canny and Zernike moments.
FIG. 6 is a graph of edge frequency versus roughness as fitted by the present invention.
Fig. 7 is a graph showing the deviation of the detected value from the actual value of the image according to the present invention.
Fig. 8 is a graph of deviation of the detection value from the actual value based on the Canny edge.
Fig. 9 is a graph of the deviation of the edge detection value from the actual value based on Zernike sub-pixels.
Detailed Description
The invention is further illustrated in the following figures and examples, which should not be taken to limit the scope of the invention.
The invention provides a method for detecting concrete surface roughness based on an image edge, which comprises the following steps:
Step1: image acquisition
And (3) carrying out image acquisition on the surface of a sample block with a known concrete roughness value by using an android smart phone with more than 1200 ten thousand pixels, wherein the roughness value is detected by a three-dimensional laser scanner.
Step 2: color image preprocessing
Since the light source is unevenly irradiated on the rough surface, an image with even illumination is obtained. Firstly, extracting illumination variable through bright-pass bilateral filtering, converting an RGB three-channel color image into an HSV channel, and estimating illumination component g (i) from the V channel:
where f (i) denotes the V channel at the i pixel position, the spatial kernel ω (i) is Gaussian, Is a single-sided Gaussian, θ and σ are standard deviations, and the Ω range is [ - ω, +ω ] 2.
The two-dimensional Gama gamma function is constructed, parameters of the two-dimensional Gama function are adjusted by utilizing the illumination distribution characteristics, the brightness value of the area with over-illumination is reduced, the brightness value of the image in the area with over-illumination is improved, and the self-adaptive correction of the image with uneven illumination is realized;
wherein F (i) is an input RGB image, g (i) is an illumination component, For the luminance average value of the illumination component, the two-dimensional gamma function O (i) realizes the non-uniformity correction of illumination by adjusting the enhancement index γ.
Step 3: graying of color images
For the processed color image, the pixel RGB values are converted into gray values by calculating the weighted sum of each pixel R, G and the B component, resulting in a gray image:
gray＝0.2989*R+0.5870*G+0.1140*B
step 4: gray scale image processing
Performing 3*3 mean filtering on the gray level image to obtain a filtered image u (x, y), and realizing the effect of smoothing noise by using the mean value of pixels around a certain pixel point; secondly, carrying out saturation processing on the lowest 1% and the highest 1% of all pixel values, setting a gray value smaller than 255 x 0.01 to 0, and setting a gray value larger than 255 x 0.99 to 255, so that the contrast ratio of an output image is improved;
Wherein p (S, t) represents a gray level image, S xy represents a filtering window with a size of 3*3 at the (x, y) position of the center point, and u (x, y) represents an image obtained after mean filtering;
Step 5: pixel-level and sub-pixel-level edge detection
Pixel-level Canny operator edge detection:
① Gaussian filter smoothing images
Convolving the two-dimensional Gaussian kernel with the gray-scale processed image, a two-dimensional Gaussian function G (x, y):
② Sobel operator calculates pixel gradients
Wherein S x and S y are Sobel operators of 3*3, I is a gray image matrix, G x and G y are gradient matrices in x-direction and y-direction, respectively, M (x, y) is a gradient magnitude matrix, and α (x, y) is a gradient angle image.
③ Non-maximum pixel gradient suppression
Taking each point in the gradient amplitude matrix M (x, y) as a central pixel point, first finding the direction d k closest to alpha (x, y). If M (x, y) is greater than two adjacent pixel values along d k, let g N (x, y) =m (x, y), otherwise g N (x, y) =0, at this time g N (x, y) is the non-maximum inhibition graph.
④ Dual threshold detection to determine true edge position edges
Setting the high threshold to be T H and the low threshold to be T L, the double thresholding is considered as a superposition of two thresholded images:
gNH(x,y)＝gN(x,y)≥TH
gNL(x,y)＝gN(x,y)≥TL
Wherein g NH (x, y) is an image with amplitude greater than a high threshold value in the non-maximum image; g NL (x, y) is an image whose amplitude is larger than the low threshold value among the non-maximum value images. And g NL (x, y) contains all non-zero pixels in g NH (x, y). All non-zero pixels coming from g NH (x, y) are deleted from g NL (x, y) by the formula.
gNL(x,y)＝gNL(x,y)-gNH(x,y)
Sub-pixel level Zernike moment (Zernike) edge detection:
the principle of sub-pixel edge positioning based on Zernike moment is that 4 parameters of a model, background gray level h, step gray level k, vertical distance l from center to edge, and angle between vertical line of edge and x-axis are calculated through three defined different orders of Zernike moment A00, A11 and A20 Comparing the parameters with a set threshold value, so as to accurately locate the edge of the image; the m-order n-th zernike moments of f (x, y) of successive images are defined as:
wherein, Is an integral kernel function;
after rotating the image, the image is symmetrical about the x-axis:
where f' (x, y) is the rotated image.
The angle of rotation phi can be calculated from the formula:
rotational invariance is an important property of the zernike moment, which is the image after rotation phi:
Then l, k, h can be calculated, then the edge positions of the sub-pixels are:
Where (x, y) is the pixel level position.
Considering the template effect, a deviation is generated in the calculation of the actual sub-pixels, and assuming that the template is m×m, the sub-pixel coordinate formula is modified as follows:
step 6: edge image logical OR operation
The edge image detected by Canny is C, the edge image detected by Zernike is Z, and the logic OR operation is carried out on C and Z.
Q＝C|Z
Wherein Q is a new image generated after logical OR operation;
step 7: calculating edge frequency
Defining the ratio of the number of white edge pixels in the image to the total pixels of the image as an edge frequency, and assuming that the resolution of the image is x×y, the edge frequency EF is:
Wherein N (1) is a white pixel and N (0) is a black pixel.
Step 8: least square method for fitting edge frequency and roughness curve
The least squares method finds the best match of the data by minimizing the sum of squares of the errors, for a given k-set of observations
Data (x i,yi) (k=1, 2, … k), solve the objective function:
Where x= [ x i,xi,…xi]T ] is the calculated edge frequency EF, ω= [ ω 1,ω2,…ωi]T ] is the undetermined parameter, and y=h (x, w) is the roughness value detected by the three-dimensional laser scanner.
After the edge frequency is calculated for all the collected concrete images with known surface roughness, and curve fitting is completed, only the steps 1 to 7 are needed to be repeated for the concrete images with unknown roughness, and the edge frequency is calculated, so that a roughness value corresponds to the curve.
In a specific embodiment, the detection method is adopted to detect the surface roughness of the concrete:
Firstly, according to the step 1, an android smart phone with more than 1200 ten thousand pixels is utilized to collect images on the surface of a sample block with known concrete roughness value, and 9 pictures are collected in total. One of the images is shown in fig. 1, and the roughness value is 2.1999 detected by a three-dimensional laser scanner.
Next, the color image is preprocessed in step 2, the illumination component is extracted by the light-passing bilateral filtering, and the image is adaptively corrected for illumination unevenness using the gamma function, as shown in fig. 2.
Next, the color image is grayed out and the gray image is processed in accordance with steps 3 and 4. The information amount in the color image is too large, and the complexity of image processing is reduced through graying.
Next, pixel-level Canny edge detection and sub-pixel-level Zernike edge detection are performed on the gray-scale image, per step 5. Pixel level and sub-pixel level detection edge maps are shown in fig. 3 and 4.
Then, according to step 6, performing logical OR operation on the Canny edge detection and the edge image detected by the sub-pixel level Zernike edge, as shown in FIG. 5;
According to step 7, the edge frequency is calculated for the new image after the logical OR operation.
Finally, according to step 8, the edge frequency and roughness curve is fitted using a least squares method, the fitted curve being as shown in fig. 6.
After the edge frequency is calculated for all the collected concrete images with known surface roughness, and curve fitting is completed, only the steps 1 to 7 are needed to be repeated for the concrete images with unknown roughness, and the edge frequency is calculated, so that a roughness value corresponds to the curve. 6 unknown images are shot, corresponding roughness values are found through calculating edge frequency, and the detection accuracy is over 94.7% through comparing actual roughness values with the actual roughness values shown in fig. 7. If only Canny edge detection or sub-pixel edge detection of the Zernike matrix is performed in step 6, many details of edge information cannot be extracted, resulting in inaccurate measurement, and the results are shown in fig. 8 and 9. The results show that the accuracy of the measurement method presented herein is improved by nearly 30% compared to the edge frequency calculated for single Canny edge detection and to the roughness fit curve. And the accuracy of the measuring method is improved by approximately 7% by fitting a relation curve with the calculated edge frequency based on the Zernike matrix.
While the present invention has been described in detail through the foregoing description of the preferred embodiment, it should be understood that the foregoing description is not to be considered as limiting the invention. Many modifications and substitutions of the present invention will become apparent to those of ordinary skill in the art upon reading the foregoing. Accordingly, the scope of the invention should be limited only by the attached claims.
Claims (3)
1. The method for detecting the concrete surface roughness based on the image edge is characterized by comprising the following steps of:
Step1: image acquisition
Image acquisition is carried out on the surface of a sample block with known concrete roughness value, and the roughness value is detected by a three-dimensional laser scanner;
Step 2: color image preprocessing
Preprocessing a color image, and correcting the problems of uneven brightness and unclear image caused by uneven illumination of the image;
Step 3: graying of color images
For the preprocessed color image, converting the RGB value of the pixel into a gray value by calculating the weighted sum of each pixel R, G and the B component to obtain a gray image;
step 4: gray scale image processing
The gray level image is subjected to mean value filtering to obtain a filtered image, and the effect of smoothing noise is achieved by using the mean value of pixels around a certain pixel point; secondly, carrying out saturation processing on the lowest 1% and the highest 1% of all pixel values, thereby improving the contrast of an output image;
Step 5: pixel-level and sub-pixel-level edge detection
Respectively adopting Canny operators to realize pixel-level edge detection on the gray level image processed in the step 4, and adopting Zernike moments to realize sub-pixel-level edge detection to respectively obtain pixel-level and sub-pixel-level edge images;
step 6: edge image logical OR operation
Performing logical OR operation on the edge images detected by the Canny edge detection and the Zernike sub-pixel edge detection;
step 7: calculating edge frequency
In the new image after logical OR operation, defining the ratio of the number of white edge pixels to the total pixels as the edge frequency,
Assuming that the image resolution is x×y, the edge frequency EF is:
Wherein N (1) is a white pixel and N (0) is a black pixel;
Step 8: fitting edge frequency and roughness curves using least squares
Fitting a relation curve of edge frequency and roughness by using a least square method based on the roughness data of all collected concrete images with known surface roughness and the calculated corresponding edge frequency data; and further, for the concrete image with unknown roughness, only the steps 2 to 7 are repeated, and the edge frequency is calculated, so that the corresponding roughness value is obtained through a relation curve.
2. The method according to claim 1, wherein in step2, the method for preprocessing the color image is as follows:
firstly, extracting illumination variable through bright-pass bilateral filtering, converting an RGB three-channel color image into an HSV channel, and estimating illumination component from the V channel;
and then constructing a two-dimensional Gama gamma function, adjusting parameters of the two-dimensional Gama function by utilizing the illumination distribution characteristic, reducing the brightness value of the area with over-illumination, improving the brightness value of the image of the area with over-illumination, and realizing the self-adaptive correction of the image with uneven illumination.
3. The method of claim 1, wherein in step 6, the method of using Zernike moments to implement sub-pixel level edge detection is as follows:
It calculates 4 parameters of model, background gray h, step gray k, vertical distance l from center to edge, angle between vertical line of edge and x-axis by defining three different order Zernike moments A00, A11, A20 Comparing the parameters with a set threshold value, so as to accurately locate the edge of the image;
The m-order n-th zernike moments of f (x, y) of successive images are defined as:
wherein, Is an integral kernel function;
after rotating the image, the image is symmetrical about the x-axis:
wherein f' (x, y) is the rotated image;
The rotation angle phi is calculated by the formula:
rotational invariance is an important property of the zernike moment, which is the image after rotation phi:
Then l, k, h are calculated, then the edge positions of the sub-pixels are:
Where (x, y) is the pixel level position.
Considering the template effect, a deviation is generated in the calculation of the actual sub-pixels, and assuming that the template is m×m, the sub-pixel coordinate formula is modified as follows:
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