JP2013238449A - Crack detection method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a crack detection method capable of accurately detecting an actual crack alone, even in the case where an imaged concrete surface includes dirt, stains, imaging irregularities, formwork marks which resemble the actual crack.SOLUTION: A crack detection method comprises the steps of: creating a wavelet image; creating a binarized image through performing a profile line tracking process; and, by referring to a feature amount table relating to blocks incorporated in the binarized image, performing a discriminant analysis with a plurality of feature amounts defined as explanatory variables and a crack or noise types defined as target variables to determine a block whose feature amount is identified as either of the crack or a noise type, performing an identification of a feature amount for every block and a discriminant analysis of each block on every block in the second step and creating a crack image.

Description

  The present invention relates to a crack detection method for detecting cracks generated on a concrete surface, and in particular, even when it is difficult to detect cracks due to dirt or lighting conditions of the photographed concrete surface, it is simple and highly accurate. The present invention relates to a crack detection method capable of detecting cracks.

  As a method for detecting cracks on the concrete surface, conventionally, a method in which an investigator visually observes using a scale and measures the width and length of the crack has been common. However, this visual observation method requires a large amount of labor and time to accurately process a large amount of information when there are large variations in accuracy due to the measurement skill of the investigator and when there are a large number of cracks. There was a problem.

  In order to solve the above problem, an image processing technique is applied in which a captured image of a concrete surface is taken into a computer, and the image is binarized into a cracked area and other areas. The image binarization process is to express the image density to 0 or 1 with respect to a certain density value. For example, 2 obtained by the binarization process for the input image f (i, j). The binarized image b (i, j) is b (i, j) = 1 (f (i, j)> k) and 0 (f (i, j) ≦ k). Here, k is a threshold value for binarization, and therefore it can be said that the quality of the binarized image is determined by the selection of the threshold value k.

  As a conventional method for obtaining a threshold value, there is a processing method using a fixed threshold value or a variable threshold value. Examples of the processing method using a fixed threshold include a P tile method, a mode method, and a method using a correlation ratio. The processing method using a fixed threshold creates a density histogram of the target image, and a bimodal histogram is obtained in which a clear valley appears between the density value of the background (concrete surface) of the image and the density value of the crack. This is an effective method in some cases.

  On the other hand, the processing method using a variable threshold is an effective method in the case where shooting unevenness occurs due to illumination conditions or the like, and the density value of the background and the density value of the target portion are not constant for the entire image. This variable threshold processing method is a method in which an average density value of a local region centered on a pixel of interest is used as a threshold. A drawback of this method is that, for example, an image having a lot of noise other than cracks is generated in accordance with a subtle change in shading of the background area.

  In the conventional image processing method, a threshold is determined for a photographed input image, and cracks are extracted while performing a binarization process. That is, the general processing flow is as follows. 1) Capture the captured image into a computer and create an input image. 2) Correct the density of the input image. 3) Perform binarization and extract cracks.

  The conventional image processing method described above can detect a crack with relatively high accuracy in detecting a crack on a concrete surface having a uniform concentration. However, the surface of an actual concrete structure contains various stains, and the density of cracks generally varies depending on the width and depth of the cracks. When a conventional image processing method is used for such a concrete surface, various problems may occur when extracting cracks. For example, in the case of the fixed threshold processing, if the dirt region and the crack region on the concrete surface have similar density values, it is extremely difficult to binarize them. Even if the density histogram exhibits bimodality and the threshold value can be easily determined, there is a very high possibility that the area that is determined to be a cracked area includes a dirty area. On the other hand, if a new threshold value is set so as not to include the dirt region around the crack, another crack region will be excluded this time.

  In the case of variable threshold processing, as the dirt on the concrete surface increases, more noise other than cracks will be included in the cracked extracted image. It is impossible to determine at all whether or not the region is a cracked region.

  In order to solve the problems of the conventional methods described above, the present inventors can easily detect cracks with high accuracy even when cracks are difficult to detect due to dirt on the concrete surface or lighting conditions. A detection method is conceived and disclosed in Patent Document 1. This crack detection method calculates wavelet coefficients corresponding to two contrasted concentrations, calculates wavelet coefficients when the two concentrations are changed, creates a wavelet coefficient table, and is a crack detection target. A step of creating a wavelet image by inputting a captured image of the concrete surface into a computer and converting the input image into a wavelet. The average density of neighboring pixels in the local area and the density of the target pixel in the wavelet coefficient table. If the wavelet coefficient of the pixel of interest is greater than the threshold, the pixel of interest is determined to be cracked, and if it is less than the threshold, the pixel of interest is determined not to be cracked. Note while changing Creating a crack extracted image by performing a comparison with the wavelet coefficients of the pixel and the threshold value, the detection method comprising.

  In this detection method, contour line tracking processing is performed on a crack detection image (binarized image) in order to remove a noise region such as a stain or dirt. The contour tracking process is a process for generating a figure as one lump (also referred to as an object) by connecting pixels having the same value. Here, a threshold is set for the area of the object, and if the area of the object is equal to or smaller than the threshold, the object is removed, and if it is larger than the threshold, the object is adopted as a crack.

  By applying the method disclosed in Patent Document 1, even when it is difficult to detect cracks due to dirt or lighting conditions on the surface of the concrete taken, compared to the processing method using the fixed threshold value or variable threshold value so far, It is possible to detect cracks more easily and with high accuracy. However, in the contour tracking process described above, if the set threshold is too large, the noise area can be reduced, but cracks in a small area may be removed at the same time, while the set threshold is small. If it is too large, cracks in small areas can be captured with high accuracy, but there is a risk that the noise area will increase, and it will be difficult and complicated to create crack images thereafter. It is.

JP 2006-162583 A

  The present invention has been made in view of the above-described problems, and even when dirt and stains similar to actual cracks, photographing unevenness, formwork traces, and the like are present on the photographed concrete surface, the actual accuracy is high. An object of the present invention is to provide a crack detection method capable of detecting only cracks.

  In order to achieve the above object, the crack detection method according to the present invention is a crack detection method for detecting cracks generated on a concrete surface, and is set by comparing the concentration of cracks with the concentration of concrete surfaces in a simulated manner. Wavelet coefficients corresponding to the two concentrations are calculated, and each wavelet coefficient is calculated when the two concentrations are changed, and a wavelet coefficient table is created, and the photographed image of the concrete surface that is the target of crack detection Is input to a computer as an input image, and a wavelet image is generated by performing wavelet transform on the input image. In the wavelet coefficient table, neighboring pixels in the local region assumed to be the density of the concrete surface. Average concentration and cracks If the wavelet coefficient corresponding to the density of the pixel of interest assumed to be the threshold is a threshold, and the wavelet coefficient of any pixel of interest in any neighboring pixel is greater than the threshold, the pixel of interest in the neighboring pixel is determined to be cracked, and any If the wavelet coefficient of any pixel of interest in the neighboring pixel is smaller than the threshold, it is determined that the pixel of interest in the neighboring pixel is not cracked, and the wavelet coefficient of the pixel of interest and the threshold are changed while changing the local region and the pixel of interest. Compare, further perform contour tracking processing, connect pixels having the same wavelet function to form one chunk, and determine if each chunk is smaller than the preset chunk threshold However, if it is larger than the lump threshold, a part of the noise is removed by determining it as a crack. A second step of creating a binarized image, specifying the feature quantity of the chunk, and referring to a feature quantity table in which noise types other than cracks and cracks are defined in advance for each feature quantity of the chunk , Using a plurality of feature quantities as explanatory variables, performing a discriminant analysis with cracks or one of the noise types as an objective variable, and determining that the mass whose feature quantity is specified is either a crack or a noise type, A third step of creating a cracked image in which noise is further removed from the binarized image by performing identification of feature values for each chunk and discriminating and analyzing each chunk in the second step. The feature table is created by executing the first step and the second step on an arbitrary concrete surface to create a binarized image and construct the binarized image. The method includes a step of calculating a plurality of feature amounts of each block by a computer and assigning an observation result (a group of cracks and noise species) to each block.

  A wavelet means a small wave, and a basic unit of a localized wave can be expressed by an expression using a wavelet function. By enlarging or reducing the wavelet function, it is possible to simultaneously analyze time information, spatial information, and frequency information. When this wavelet coefficient is applied to a concrete surface having cracks, the characteristic of this coefficient is that it depends on the concentration of the concrete surface, the concentration of cracks, and the crack width. For example, the value of the wavelet coefficient tends to increase as the crack width increases, and the value of the wavelet coefficient tends to increase as the density of cracks increases (closer to black).

  An algorithm for detecting cracks using wavelet coefficients calculated by wavelet transform is as follows. First, the wavelet coefficient is obtained from the inner product of the captured image of the concrete surface and the wavelet function. By converting this wavelet coefficient to 256 gradations, a wavelet image having a continuous amount can be created.

  As described above, the wavelet coefficient varies depending on the crack width, crack density, and concrete surface density. As a result, the wavelet coefficient related to the crack density and concrete surface density for each gradation is created using simulated data. The wavelet coefficient table is created in advance. The wavelet coefficient for each gradation in the wavelet coefficient table serves as a threshold for crack detection. For example, wavelet coefficients (thresholds) corresponding to two contrasted concentrations (one concentration can be assumed to be a concrete surface concentration and the other concentration to be a crack concentration) are unique if the wavelet coefficient table is referenced. To be determined. Therefore, as will be described later, when the wavelet coefficient between the two densities to be compared in the captured image is calculated, if this wavelet coefficient is larger than the threshold value of the wavelet coefficient table, it can be determined that it is a crack, and the threshold value If it is smaller than that, it can be determined that it is not cracked.

  The pseudo data when creating the wavelet coefficient table is not particularly limited. For example, the crack width is 1 pixel (1 pixel) to 5 pixels (5 pixels), and cracks of each pixel width are included. For each, the wavelet coefficient corresponding to the gradation of the concrete surface and the gradation of the crack is calculated. When setting the threshold value, for example, the wavelet coefficient corresponding to the crack is selected from the wavelet coefficients when the crack width is 1 pixel, and the wavelet coefficient of the portion that is not the crack area is selected among the wavelet coefficients when the crack width is 5 pixels. A coefficient is selected, and an average value of these two wavelet coefficients can be used as a threshold value in an arbitrary gradation.

  In the crack detection method of the present invention, first, in the first step, the wavelet coefficient table described above is created, and correction processing such as lens aberration correction and tilt correction is performed on the captured image, and this is performed on the computer. The input image is used as an input image, and a wavelet image is created by wavelet transforming the input image. The wavelet image is created in the computer as follows. First, wavelet coefficients are calculated for an appropriately set wide area (for example, an area of 30 × 30 pixels). Next, it is the same in a wide area moved by one pixel from this wide area (in the same way, for example, a 30 × 30 pixel area and most of the pixels are the same as the 30 × 30 pixel area before the movement) The wavelet coefficient is calculated as follows. By repeating this operation for the entire input image, a wavelet image composed of continuous amounts of wavelet coefficients is created inside the computer.

  Next, in a second step, in the wavelet image composed of continuous wavelet coefficients, the threshold value (wavelet coefficient) in the wavelet coefficient table is compared with the wavelet coefficients constituting the wavelet image, and the wavelet coefficients constituting the image are determined. If it is larger than the threshold value, it is determined as a crack (for example, white on the screen), and if it is smaller than the threshold value, it is determined that it is not a crack (for example, black on the screen). Then, by performing this operation on the entire wavelet image, a binarized image in the middle stage in which white cracks are drawn in the black background color is created.

  A contour image tracking process, which is a process of removing a part of noise, is performed on a pixel that is determined to be a crack in the binarized image in the middle stage to create a binarized image.

  In this contour tracking process, in a binary image at an intermediate stage, pixels having the same wavelet function are connected to form one lump, and each lump is smaller than a preset lump threshold Is determined to be noise, and if it is larger than the lump threshold, a part of the noise can be removed by determining cracking.

  As described above, the binarized image created in the second step contains a large number of objects (objects) formed by connecting pixels having the same wavelet function as described above.

  By using a large number of masses built into this binarized image, from the mass judged to be cracked in the binarized image, it is not actually cracked, but it is similar to cracks. The third step is to create a cracked image by removing noise species such as frame marks.

First, the feature amount of the lump is specified. Here, the feature amount of a lump is a factor that affects the shape of the lump. For example, the lump area, the height and width of the lump's circumscribed rectangle, and the major axis of the oval corresponding to the lump's outline And the minor axis, the ratio of the area of the lump and its circumscribed rectangle, the ratio of the width and height of the circumscribed rectangle of the lump, the ratio of the major and minor axes of the oval corresponding to the outer shape of the lump, the roundness of the lump ( (Perimeter length) ² ÷ (4π × area) The roundness of the lump calculated from the general formula, the irregularity of the lump outer circumference (fractal dimension), the length of the lump outer periphery, and the like.

  Regarding the feature quantities of these blocks, a feature quantity table is created in advance. The feature table may be created on a concrete surface that is currently subject to crack detection on the concrete surface, or on any other concrete surface. The first step and the second step are performed in the same manner to create a binarized image, and a plurality of feature amounts of each block constituting the binarized image are calculated by a computer and tabulated.

  The feature table further includes an observation result regarding this arbitrary concrete surface, that is, a classification “3” when each lump is cracked, and a classification “2” when the noise type is a formwork or a scar. In the case of noise such as stains and stains, among the noise types, it is assigned to category “1”, etc., and it is assigned to which category each group of observation results belongs to a group consisting of cracks and noise species. It has been.

  Discriminant analysis is performed on this feature quantity table with a plurality of feature quantities of each block as explanatory variables, and cracks or one of the noise types as an objective variable. It is determined that it is one of the noise types, and the determined result is used as a predicted value.

  For example, if the predicted value for a mass that is actually cracked is cracked, the predicted value matches the observed result, and the predicted value for a mass that is not actually cracked (a type of noise type) is cracked. If so, the predicted value and the observation result do not match.

  Here, “discriminant analysis” is a standard for discriminating which group is included when new data is obtained when it is clear that the pre-given data is divided into different groups. Is a technique for obtaining a discriminant function.

  In the present invention, a plurality of feature values of each lump are used as explanatory variables, and cracks or one of noise types is used as an objective variable.

  Noise is further removed from the binarized image by performing feature identification and discriminant analysis for each of a large number of chunks incorporated in the binarized image identified in the second step. A cracked image can be created.

  When the removal of noise is completed and a crack image is created, the crack width is estimated. Here, an embodiment of the crack width estimation method will be described. When estimating the crack width, unlike the concrete surface to be photographed this time, it is a photographed image of the crack scale that has been pasted on the concrete surface of the photographed object, and the crack scale already accumulated. A photographed image is set as a database. The crack scale photographed image database includes a plurality of actual size values q from the minimum width to the maximum width of the crack scale, and further from a plurality of image data having different photographing distances, lens focal lengths, and spatial resolutions. It is composed.

  On the other hand, the crack image created in the third step on the concrete surface of the crack identification target is further thinned to form the center line of the cracked thin line of the concrete surface to be photographed. Create an image.

  The crack scale photographed image in the database is input to the computer as a crack scale input image, and the crack scale wavelet image is created by wavelet transform of the crack scale input image. The crack-scale thinned image is formed by executing the processing, and the explanatory variable p of the crack width estimation formula is set to p = (wavelet coefficient value) in the crack-scale thinned image. ) − (Threshold of wavelet coefficient), and regression analysis is performed from the actual size value q of the crack scale specified from the captured image of the crack scale and the explanatory variable p. Then, the following crack width estimation formula with the explanatory variable p as a parameter: y = a + bp (a and b are constants specified by regression analysis, p is an explanatory variable), and for the estimation formula p, By applying p specified from the wavelet coefficient value and the threshold value of the wavelet coefficient in the thinned image of the crack on the concrete surface to be imaged, the crack width of each concrete surface is specified for each object to be imaged.

  Here, the crack width estimation formula specified by the regression analysis varies depending on the amount of data of the accumulated crack scale and the like. The explanatory variable p is a value corresponding to the total number of pixels of the thinned image on the crack scale.

  As a result of the regression analysis, it is possible to create a database composed of crack width at crack scale, wavelet coefficient, luminance of target pixel, average luminance of surrounding pixels, spatial resolution and correlation coefficient.

  In addition, as a result of the regression analysis, the crack width estimation formula may be obtained using only a photograph image of a crack scale having a correlation coefficient of a certain height or higher. The accuracy of the estimation formula largely depends on the correlation coefficient obtained by the regression analysis. More specifically, the regression analysis is performed using only the photographed image of the crack scale having a certain high correlation coefficient. It is desirable to specify an estimation formula for the crack width. As a result, it is possible to remove a blurred image that depends on a problem of focus when the crack scale is photographed or a sticking method of the crack scale is floating, and as a result, the accuracy of the estimation formula is improved.

  Finally, crack data based on the estimated crack width, that is, calculation of total crack length, calculation of crack length for each crack width, creation of distribution map for each crack width, creation of crack development chart, etc. Desired information can be extracted from the crack data.

  According to the crack detection method of the present invention, after performing contour tracking processing in the second step to remove a part of noise, discriminant analysis is further performed in a third step to further remove noise. Since the crack image is created, the crack width estimated from the created crack image has extremely high accuracy, and various types of crack data other than the crack width can be obtained with high accuracy.

  Further, in a preferred embodiment of the crack detection method according to the present invention, the distance between each lump and the position of the center of gravity of the group in the feature quantity table is specified by the Mahalanobis general distance with respect to the feature quantity table. The probability value that a lump belongs to each group is specified from the Mahalanobis's general distance, and the determination result of each lump is corrected based on the probability value.

  The discriminant functions include a linear discriminant function based on a hyperplane / straight line and a non-linear discriminant function based on a Mahalanobis general distance based on a hypersurface / curve. In this embodiment, the distance between each block and the center of gravity position of the group in the feature table is specified by this Mahalanobis general distance, and the probability value that the block belongs to each group is specified from this Mahalanobis general distance. Then, the determination result of each lump is corrected based on this probability value.

  In this way, by correcting the determination result of each block based on the probability value, the predicted value for each block can be corrected to a corrected predicted value, and a crack image based on the corrected predicted value can be created. The detection accuracy is further improved.

  And regarding this probability value, a threshold value of the probability value is defined in advance, and if the specified probability value falls below this threshold value, it does not belong to any group, and for a mass that does not belong to any group For example, the group of the chunks may be specified artificially.

  In other words, for those that do not belong to any group, it is more efficient for the observer (engineer) to make a judgment based on the captured image and the probability value, rather than applying some algorithm to the computer. This is because the accuracy of the results is favorable.

  As can be understood from the above description, according to the crack detection method of the present invention, the binarized image is subjected to the contour tracking process to remove a part of the noise to create the binarized image. By performing discriminant analysis with reference to a feature amount table set in advance for the binarized image and further removing noise to create a cracked image, it is possible to detect a crack with extremely high accuracy.

(A) is the schematic diagram which showed the relationship between an input image and a local region, (b) is the schematic diagram which showed the relationship between a local region and an attention pixel. It is the flow which showed the crack detection method of the present invention. It is the figure which showed the pseudo image. FIG. 4 is a bird's-eye view of wavelet coefficients of the pseudo image of FIG. 3. It is the figure which showed one Embodiment of the wavelet coefficient table. (A) is the photograph figure which showed the picked-up image, (b) is the figure which showed the binarized image and also the crack image. (A) is the figure explaining the feature-value table, (b) is a table which showed the correctness determination of the predicted value based on discriminant analysis, (c) is the predicted value corrected by applying a probability value further It is the table which showed right / wrong discrimination of. (A) is the photograph figure which showed the picked-up image, (b) is the figure which showed the binarized image and also the crack image. (A) is a figure explaining the feature-value table which has the corrected prediction value, (b) is a table which showed the correctness determination of the prediction value corrected by applying the probability value. It is a flowchart of embodiment of the calculation method of the estimated value of the crack width in the flow of FIG. It is a flowchart of other embodiment of the calculation method of the estimated value of a crack width.

  Hereinafter, embodiments of a crack detection method of the present invention will be described with reference to the drawings. FIG. 1 a is a schematic diagram showing a relationship between an input image and a local region. In the crack detection method of the present invention, wavelet transform is performed in the wide area 2 in the input image 1, and cracks are detected in the local area 3 that is the center of the wide area 2. The wide area 2 is translated vertically and horizontally throughout the input image 1 to detect cracks in the input image 1. By this method, it is possible to detect cracks with higher accuracy as compared with the method of determining one threshold value in the input image 1 as in the conventional fixed threshold method.

  FIG. 1 b is an enlarged view of the local region 3. In the illustrated embodiment, for example, nine pixels of 3 × 3 (eight neighboring pixels 31, 31... And a target pixel 32 located in the center) are targeted. As a crack judgment. The wavelet coefficient is calculated for the wide area 2 in FIG. 1a. Here, a calculation formula for calculating a wavelet coefficient by performing wavelet transform using a wavelet function (mother wavelet function) is shown below.

Here, f (x, y) is an input image (where x and y are arbitrary coordinates in the two-dimensional input image), Ψ is a mother wavelet function (Gabor function), and (x ₀ , y ₀ ) is the translation amount of Ψ, a ^k is the expansion or contraction of Ψ (where a ^k is the reciprocal of the frequency, and the frequency width for calculating several frequency regions is indicated by an integer k) value), rotate the f ₀ is the center frequency, the σ is the standard deviation of the Gaussian function, the rotation angle theta is representative of the direction of travel of the wave, (x ', y') is the angle theta (x, y) Each coordinate is shown.

Here, the cumulative value C (x ₀ , y ₀ ) of the wavelet coefficient Ψ is obtained for a plurality of θ, k calculated using Formula 1 as Formula 4.

The above parameters can be set arbitrarily. For example, σ can be set to 0.5 to 2, a ^k to 0 to 5, f ₀ to 0.1, and the rotation angle to 0 to 180 degrees. .

The parallel movement amount (x ₀ , y ₀ ) in Equation 4 corresponds to the position of the target pixel, and the continuous amount of wavelet coefficients (C (x ₀ , y ₀ ) is obtained by sequentially moving the position of the target pixel. )) Can be calculated, and a wavelet image can be created by illustrating this continuous quantity.

  After calculating the wavelet coefficients for all the pixels constituting the wide area 2 based on the above calculation formula, the wavelet coefficients are similarly applied to all the pixels in the wide area 2 formed by moving one pixel of interest right and left or up and down. Calculate. By performing this wavelet coefficient calculation on the entire input image, it is possible to create a wavelet image (an image made up of a continuous amount of wavelet coefficients) in which the constituent pixels within an appropriate range have respective wavelet coefficients.

  Next, an embodiment of a crack detection method will be described with reference to FIG. An input image is created (step S10) by taking a photographed image of the concrete surface photographed by a digital camera such as a CCD camera into a computer.

  Next, wavelet coefficients are calculated for a pseudo image having two contrasts that have no relationship with the input image. For example, as shown in FIG. 3, the background color a assumed to be a concrete surface (for example, the background colors R, G, B are assumed to be 255, 255, 255) and the line segments b1 to b1 assumed to be cracks. The wavelet coefficient of the pseudo image consisting of b5 is obtained. Here, the line segments b1 to b5 change in order from 1 pixel (1 pixel) to 5 pixels (5 pixels) in order, and each line segment has three types of density (for example, In the line segment b1, it changes to b11 (black), b12 (light black), and b13 (gray) in descending order of density). FIG. 4 shows a bird's-eye view of wavelet coefficients calculated by performing wavelet transform on this pseudo image. In FIG. 4, the X axis indicates the line segment width, the Y axis indicates the color density of the line segment, and the Z axis indicates the wavelet coefficient. The line width may be set according to the maximum crack width to be finally extracted. For each pixel width, the wavelet coefficient of the crack region and the wavelet coefficient other than the crack region can be calculated.

  In this embodiment, when calculating the arbitrary density (gradation) assumed to be a concrete surface and the threshold value (wavelet coefficient) corresponding to the arbitrary density (gradation) assumed to be a crack, for example, the crack width is Threshold value corresponding to the target gradation within the set crack width range, with the average value of the wavelet coefficient of the crack area in the case of 1 pixel width and the wavelet coefficient other than the crack area in the case of the crack width of 5 pixels. It is said. Of course, this threshold value may be arbitrarily set.

  By creating a combination of the two densities to be compared with 256 gradations of 0 to 255, a wavelet coefficient table as shown in FIG. 5 is created (step S30 in FIG. 2). Note that this operation does not have to be performed at the illustrated flow position, and may be performed, for example, before creation of the input image.

  Returning to FIG. 2, a wavelet image is created (step S20) by wavelet transforming the input image. A wavelet image is composed of a continuous amount in which each pixel has a unique wavelet coefficient as described above. By comparing the wavelet coefficient of each pixel with the wavelet coefficient (threshold) of the corresponding wavelet coefficient table, the wavelet image is black. A binarized image in the middle stage in which white cracks are drawn in the background color (binarized image from which a part of noise is not removed) is created.

  A contour tracking process is performed on this image to remove a part of noise, and a binarized image is created. In the specifying method of the present invention, a stage until the generation of a crack image in the third step, specifically, a part of noise is removed from the image in the second step to create a binary image, In step 3, the remaining noise is further removed from the binarized image to create a cracked image. Here, the method of the contour line tracking process connects pixels having the same wavelet function to form one lump, and if each lump is smaller than a preset lump threshold, it is determined as noise, If it is larger than the lump threshold, this is a method for determining a crack. As a method of connecting pixels to form one block, starting from an arbitrary cracked pixel in each cracked area (first pixel), for example, paying attention to a pixel adjacent counterclockwise from this first pixel, When the adjacent pixel (second pixel) is a cracked pixel, the first pixel and the second pixel are connected. Thereafter, similarly, the second pixel, the third pixel,..., The (n−1) th pixel, the nth pixel and the cracked pixel are traced, and when the first pixel that is the starting point comes after the nth pixel, One pixel to n-th pixel are determined as one crack location (crack line). Alternatively, when there is no crack pixel following the nth pixel, the first pixel to the nth pixel are determined as one crack location (crack line).

  By the way, apart from the processing flow up to the creation of the binarized image (step S40), the feature amount table based on the binarized image is created independently in the specifying method of the present invention.

Specifically, a binarized image is created by executing up to step S40 in FIG. 2 on a concrete surface different from the concrete surface to be cracked. This binarized image contains a large number of chunks (objects) formed by connecting pixels having the same wavelet function, and some feature quantities of each chunk are identified and stored in the feature quantity table. As a table. Here, the feature amount of the lump is a factor that affects the shape of the lump, and the area of the lump, the height and width of the circumscribed rectangle of the lump, the major and minor axes of the ellipse corresponding to the outer shape of the lump, The ratio of the area of the lump to its surrounding rectangle, the ratio of the width and height of the lump's circumscribed rectangle, the ratio of the major axis to the minor axis of the oval corresponding to the outer shape of the lump, the roundness of the lump ((perimeter length) ² ÷ (4π × area) The roundness of the lump calculated from the general formula), the roughness of the lump's outer periphery (fractal dimension), the length of the lump's outer periphery, etc.

  And the observation result regarding this arbitrary concrete surface is further input into this feature-value table. Specifically, when the lump is cracked, the classification is “3”, among the noise types, the classification is “2”, and among the noise types, the classification is “stains or dirt”. A number such as “1” is defined, and it is assigned to which of the groups of cracks and noise species each lump as an observation result belongs (step S50).

  Next, with reference to this feature amount table, a discriminant analysis is performed on a large number of masses contained in the binarized image created for the concrete surface to be cracked, and a predicted value is obtained. . More specifically, a discriminant analysis is performed by using a plurality of feature quantities of each lump as explanatory variables and cracking or any noise type as an objective variable. It is determined that it is one of the noise types, and the determined result is set as a predicted value (step S51).

  Prediction values based on discriminant analysis of a large number of objects (objects) built in the binarized image for the concrete surface to be cracked are added to the feature table created in step S50, and the feature table becomes It will be updated sequentially. Note that the actual crack observation results for the concrete surface subject to crack identification are also input to the feature quantity table.

  The predicted value of each block is specified by discriminant analysis and automatically input to the feature table in the computer, but the remaining noise is the one whose predicted value does not match the observed value.

  This noise is corrected using the probability value (calculation of the probability value is step S52), the predicted value is corrected and the feature amount table is updated, and this content is reflected in the binarized image S40 to remove the remaining noise. Then, a crack image is created (step S60).

  Here, a method of applying a probability value to a predicted value and correcting the predicted value will be described with reference to FIGS. 6A is a photograph showing a photographed image, and FIG. 6B is a diagram showing a binarized image and a cracked image (in FIG. 6B, the binarized image is only a black and white image, and a cracked image is shown). Is an image in which line types of sections 1, 2, and 3 are specified for the binarized image). FIG. 7A is a diagram for explaining the feature amount table, FIG. 7B is a table showing correctness / incorrectness determination of a predicted value based on discriminant analysis, and FIG. 7C is a table of predicted values corrected by applying a probability value. It is a table showing correct / incorrect discrimination.

  In the cracked image of FIG. 6b, the section “2” is a “region such as a mold or a scar”, the section “3” is a “crack region”, and the other is a “noise region such as a stain or a stain”. Category "1" is defined.

  In the feature quantity table shown in the figure, the first row of the matrix in FIG. 7b represents the number of the predicted values that have become the determination categories “1” to “3” with respect to the observation value category “1”. For example, the number of correct discriminations in which the observed value category “1” becomes the predicted value judgment category “1” is 3275, and the number of misjudgments in which the observed value category “1” becomes the predicted value judgment category “2”. Represents 197, and the number of misclassifications in which the observed value category “1” becomes the predicted value judgment category “3” represents 75. The correct discrimination rate and the incorrect discrimination rate are 92.3%, 5.6%, and 2.1%, respectively. Similarly, the second row and the third row of the matrix of FIG. 7b are the numbers of the correct discrimination or the erroneous discrimination of the prediction value determination categories “1” to “3” for the observation value categories “2” and “3”. .

  As considerations regarding the results of discriminant analysis, the observed value category “3” → the predicted value judgment category “3” is 71.5%, the observed value category “3” → the predicted value judgment category “2” is 13.1%. %, Observed value category “3” → predicted value judgment category “1” is 15.4%. Here, the reason why the observed value classification “3” → predicted value judgment classification “1” or “2” is the case where cracks and cracks, or spots such as spots are combined, or crack detection is insufficient. This is because it becomes discontinuous, and the misclassification rate tends to be high for objects in a partial area of the crack. Also, the observed value category “1” → the predicted value judgment category “1” is 92.3%, the observed value category “1” → the predicted value judgment category “2” is 5.6%, and the observed value category “1” → predicted value judgment category “3” is 2.1%. Moreover, the observation value category “2” → the prediction value judgment category “1” is 14.4%, the observation value category “2” → the prediction value judgment category “2” is 74.3%, and the observation value category. “2” → predicted value determination category “3” is 11.3%. The misclassification rate of the observed value category “1” → predicted value judgment category “2” or “3” is extremely rare, but may occur due to dirt or patterns on the concrete surface. Misclassification of observed value category “2” → predicted value judgment category “1” or “3” may occur in part of spot detection, form trace, and crack detection on the concrete surface.

  In response to such a determination result, the predicted value is corrected by applying the probability value. The correction of the predicted value will be described with reference to FIGS. 7c, 8 and 9. FIG. FIG. 8A is a photograph showing a photographed image, FIG. 8B is a diagram showing a binarized image and a cracked image, and FIG. 9A is a diagram explaining a feature amount table having a corrected predicted value. Yes, FIG. 9B is a table showing correctness / incorrectness determination of the predicted value corrected by applying the probability value.

  When the observed value classification is “3” → the predicted value judgment classification is “1” or “2”, there is a need for improvement in order to erroneously determine that the crack is not a crack. Therefore, it is determined which group each object (object) belongs to based on the Mahalanobis general distance. Since it is not clear how much the group belongs to this method, the probability that each lump belongs to each group is calculated using Mahalanobis's general distance. As for the Mahalanobis generalized distance, we referred to “Multivariate Analysis Method (Tadakazu Okuno et al.) Nisshingi Renren”.

  Further, the probability density of the p-dimensional normal distribution can be written as the following expression 7.

If Equation 7 is rewritten using Mahalanobis's generalized distance D ² , the following Equation 8 can be obtained.

D ² defined in Equation 6 follows a χ ² distribution with p degrees of freedom. The probability that D ² is greater than a certain value can be obtained using the χ ² distribution.

  In order to reduce the misclassification of the observation value classification “3” → the prediction value judgment classification “1” or “2” by examining the misclassification rate of the feature value data using the probability value thus obtained. The predicted value correction method will be described below.

  A threshold is defined as 0.6 for the probability value, and if the probability of belonging to each group of an object is 0.6 or less, a discrimination category “others” is added to the predicted value and this discrimination category is added. Shall belong. An engineer determines an object belonging to this discrimination category “others” based on a captured image and a probability value.

  The discriminant analysis result based on the feature value data after the prediction value correction is shown in FIG. 7c. Based on this result, the handling of objects that do not belong to any group of the judgment value classification categories “1-3” was examined. When the predicted value does not belong to any group of the determination categories “1 to 3”, the determination category “others” is added to the predicted value, and when the object enters the determination category “others”, the predicted value is calculated from the probability value. Calculate the correction value.

  Regarding the corrected predicted value, in the display of “2-1-3” in FIG. 9A, the probability value is high in the order of probability value P2 → P1 → P3. Therefore, the engineer makes a final judgment with reference to this value. I will give you.

  FIG. 8B shows a binarized image with respect to the photographed image of the crack identification target shown in FIG. 8A, and FIG. 8B further shows a crack image containing an object whose predicted value is corrected (FIG. 8B). In 8b, the binarized image is only a black and white image, and the cracked image is an image in which the line types of sections 1 to 4 are specified for the binarized image). Further, FIG. 9b shows the discriminant analysis result after the predicted value is corrected by applying the probability value. Here, in the crack image shown in FIG. 8b, the object of the determination category “3” is the region determined to be a crack, the object of the determination category “2” is the region determined to be a formwork trace, a scar, a reinforcing bar trace, etc. The object of the classification “4” represents the area where the determination is suspended, and the other objects represent the areas determined as noise such as dirt and patterns on the concrete surface (determination classification “1”).

  Some objects combined with cracks and cracks or peeling marks are misclassified as judgment category “2”, and the left and right blot marks on the outer frame are misclassified as cracks. Compared to the extraction method, the processing time and human error can be reduced when crack extraction is performed by the specific method of the present invention. That is, in the creation of the cracked image in step S60 in FIG. 2, the recognition rate for the determination category “2” such as the formwork trace and the scar is 48.5%, finally determined from the misclassification rate in FIG. It is extremely low, and most of the misclassification rates are judged as cracks.

  In the conventional method, since the crack image created at the final stage of the creation of the crack image in step S60 in FIG. 2 is manually performed using image editing software, this operation is extremely complicated and time-consuming. In the operation process, human error such as oversight of cracks was likely to occur. On the other hand, such an artificial mistake is eliminated by performing a discriminant analysis and automatically processing the crack determination while correcting the predicted crack value using the probability value.

  Next, for the created cracked image, a thinned image is formed which is composed of the center line of each crack and has, for example, the entire crack having one pixel width (one pixel width) (step S70).

  An estimated value of crack width is calculated using the thinned image (step S80). Here, a method for estimating the crack width will be described with reference to FIG.

  The crack scale wavelet image is generated by inputting the photographed image of each crack scale stored in the database in step S100 to a computer to create an input image of each crack scale, and wavelet transforming the input image of each crack scale. (Step S101), and the wavelet coefficient of each pixel of the wavelet image is compared with the wavelet coefficient (threshold value) of the corresponding wavelet coefficient table to create a binary image of the crack scale (step S102). Then, thinning processing is executed on this to create a crack scale thinned image composed of the center line (step S103).

  In the created crack scale thinned image, an explanatory variable p of the crack width estimation formula is defined by the following formula.

  Regression analysis is performed from the explanatory variable p described above and the actual size value q of the crack scale specified from the photographed image of the crack scale.

  As a result of the regression analysis, the following crack width estimation formula using the explanatory variable p as a parameter can be obtained (step S104).

  By applying p specified from the wavelet coefficient value and the wavelet coefficient threshold value in the thinned image of cracks on the concrete surface to be photographed to p in the estimation formula, the crack width of each concrete surface for each object to be photographed Can be specified (step S110).

  Returning to FIG. 2, after estimating the crack width in step S80, a table and drawings (crack development etc.) relating to the distribution state of each crack, the number of cracks for each crack width, etc. are created as data (step S90).

  In FIG. 10, the flow from the crack scale database to the identification of the crack width estimation formula and the creation of the thinned image of the concrete surface to be imaged are shown in a parallel flow diagram. The method is not limited to this flowchart. For example, steps S100 to S104 are performed in advance, an estimation formula for the crack width is specified in advance, and then the thinning on the concrete surface to be photographed is performed. It may be a flow for creating an image.

  Here, as a result of the regression analysis, it is desirable to perform a regression analysis using only a crack scale image having a relatively high correlation coefficient to specify an estimation formula for a crack width.

  For example, regression analysis is performed using a crack scale image having a correlation coefficient of 0.85 or more, and the specified crack width estimation formula (for example, y = 0.029 + 0.000736p) can identify the crack width with extremely high accuracy. it can.

  FIG. 11 is a diagram illustrating another embodiment relating to a crack width estimation method.

  The p obtained from the captured image tends to vary depending on the quality of the captured image. Therefore, in order to standardize the quality of the captured image with a certain standard, a standardized p represented by the following equation is obtained.

  The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and there are design changes and the like without departing from the scope of the present invention. However, they are included in the present invention.

DESCRIPTION OF SYMBOLS 1 ... Input image, 2 ... Wide area | region, 3 ... Local area | region, 31 ... Neighboring pixel, 32 ... Interesting pixel

Claims (3)

A crack detection method for detecting cracks occurring on a concrete surface,
Estimate the density of the crack and the concrete surface, calculate the wavelet coefficients corresponding to the two contrasted levels, and calculate the wavelet coefficients for each of the two concentrations. A first step of creating a wavelet coefficient table, inputting a photographed image of a concrete surface to be detected for cracking into a computer as an input image, and generating a wavelet image by wavelet transforming the input image;
In the wavelet coefficient table, the average density of neighboring pixels in the local area assumed to be the density of the concrete surface and the wavelet coefficient corresponding to the density of the target pixel assumed to be the crack density are used as threshold values, and in any neighboring pixels If the wavelet coefficient of any pixel of interest is larger than the threshold, the pixel of interest in the neighboring pixel is determined to be cracked, and if the wavelet coefficient of any pixel of interest in the neighboring pixel is smaller than the threshold, The target pixel is determined not to be cracked, the local region and the target pixel are changed, the wavelet coefficient of the target pixel is compared with a threshold value, and the contour tracking process is further performed to connect pixels having the same wavelet function. Form one lump and each lump is preset. If smaller than the threshold value of the mass are determines the noise, by if greater than the threshold value of the mass is determined that crack, the second step of creating a binarized image in which a part of the noise is being removed,
Identify the mass feature quantity,
Discriminant analysis with multiple feature quantities as explanatory variables and cracks or one of the noise types as an objective variable by referring to a feature quantity table in which cracks and noise types other than cracks are defined in advance for each feature quantity of a block And determine that the mass whose feature value is specified is either a crack or a noise species,
A third step of creating a cracked image in which noise is further removed from the binarized image by performing identification of feature values for each chunk and discriminating and analyzing each chunk in the second step. Consists of
The feature amount table is created by executing the first step and the second step on an arbitrary concrete surface to create a binarized image, and a plurality of the respective blocks constituting the binarized image. A crack detection method comprising a step of assigning an observation result (one of a group consisting of cracks and noise seeds) to each lump while calculating the feature amount of the lump with a computer.   The distance between each block and the center of gravity of the group in the feature table is specified by the Mahalanobis general distance with respect to the feature table, and the probability value that the cluster belongs to each group is determined from the Mahalanobis general distance. The crack detection method according to claim 1, wherein the crack detection method is specified and the determination result of each lump is corrected based on the probability value.   A threshold value of the probability value is defined, and if the specified probability value is lower than the threshold value, it does not belong to any group, and for a chunk that does not belong to any group, artificially The crack detection method according to claim 2, wherein a group is specified.

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