Reinforcement inspection device, reinforcement inspection method, and program

Abstract

To provide a reinforcing bar inspecting device, a reinforcing bar inspecting method, and a program, capable of determining a reinforcing bar diameter even for a reinforcing bar having the same knot intervals.SOLUTION: A reinforcing bar inspecting device (1) obtains scan lines formed of pixel values of a plurality of pixels arranged along a longitudinal direction of a reinforcing bar, for each pixel in a direction orthogonal to the longitudinal direction of the reinforcing bar, calculates a feature amount obtained by subjecting the pixel values of the scan lines to frequency conversion, identifies a boundary position between the reinforcing bar and a back ground on the basis of a result obtained by collating the calculated feature amount with a reference feature amount, uses the boundary position to identify a first reinforcing bar diameter, uses a learning model to infer a second reinforcing bar diameter, and uses the first reinforcing bar diameter and the second reinforcing bar diameter to determine the reinforcing bar diameter as a measurement result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a reinforcement inspection device, a reinforcement inspection method, and a program. [Background technology]

[0002] In the construction of reinforced concrete structures, after the rebars have been assembled and arranged, a rebar arrangement inspection is performed to check whether the rebars have been arranged as designed. For example, Patent Document 1 describes a rebar arrangement confirmation support system that uses photographed images of arranged rebars to identify the diameter of the rebars. The system acquires a one-dimensional distribution of brightness values ​​of pixels aligned along the longitudinal direction of the rebars from the photographed images of the arranged rebars, and identifies the node spacing of the rebars using a waveform obtained by frequency analysis of the one-dimensional distribution. The system then identifies the name corresponding to the identified node spacing as the diameter of the rebars from among the names (rebar diameters) associated with each of a plurality of node spacings in the JIS standard. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-21622 Summary of the Invention [Problem to be solved by the invention]

[0004] However, since there is not necessarily a one-to-one correspondence between rebar diameter and node spacing, the rebar arrangement confirmation support system described in Patent Document 1 has the problem of being unable to accurately determine the rebar diameter. For example, in the case of threaded rebars, where nodes are formed in a screw shape, different names may be associated with the same node spacing (thread pitch).

[0005] The present disclosure is intended to solve the above-mentioned problem, and aims to provide a reinforcing bar inspection device, a reinforcing bar inspection method, and a program that can determine the reinforcing bar diameter even for reinforcing bars with the same node spacing. [Means for solving the problem]

[0006] The reinforcement inspection device according to the present disclosure includes a three-dimensional information acquisition unit that acquires three-dimensional information of an inspection area where reinforcing bars are arranged, a plane identification unit that uses the three-dimensional information to identify the reinforcement plane of the inspection target from the inspection area, an image conversion unit that converts the captured image of the inspection area into an orthogonal image that is oriented squarely to the reinforcement plane of the inspection target, a position detection unit that uses the orthogonal image to detect reinforcing bar position information in the reinforcement plane, an image extraction unit that extracts a reinforcing bar image from the orthogonal image based on the reinforcing bar position information in the reinforcement plane of the inspection target, and a scanning unit that extracts a scanning line consisting of pixel values ​​of a plurality of pixels aligned along the longitudinal direction of the reinforcing bar in the orthogonal image. The system is equipped with a feature calculation unit that acquires images for each pixel in a direction perpendicular to the longitudinal direction of the rebar, calculates feature values ​​by frequency-converting the pixel values ​​of the scanning line, identifies the boundary position between the rebar and the background based on the results of comparing the calculated feature values ​​with reference feature values, and identifies the first rebar diameter using the boundary position; an inference unit that infers the second rebar diameter using a learning model that outputs the rebar diameter when a rebar image is input; a rebar diameter determination unit that determines the rebar diameter of the measurement result using the first rebar diameter and the second rebar diameter; and a measurement result information generation unit that generates and outputs measurement result information to display the rebar diameter of the measurement result. [Effects of the Invention]

[0007] According to the present disclosure, a scanning line consisting of pixel values ​​of multiple pixels aligned along the longitudinal direction of the rebar is acquired for each pixel in a direction perpendicular to the longitudinal direction of the rebar, the pixel values ​​of the scanning line are frequency-converted to calculate feature amounts, the calculated feature amounts are compared with reference feature amounts to identify the boundary position between the rebar and the background based on the results, the boundary position is used to identify the diameter of a first rebar, a learning model is used to infer the diameter of a second rebar, and the first and second rebar diameters are used to determine the diameter of the measured rebar. As a result, the rebar arrangement inspection device according to the present disclosure can determine the diameter of rebars even if they have the same node spacing. [Brief explanation of the drawings]

[0008] [Figure 1]1 is a block diagram showing the configuration of a bar arrangement inspection device according to a first embodiment. [Figure 2] 3 is a flowchart showing a reinforcement bar arrangement inspection method according to the first embodiment. [Figure 3] FIG. 10 is an explanatory diagram showing an overview of the photographing process of the reinforcement inspection area. [Figure 4] 4A, 4B, and 4C are graphs showing the relationship between a function indicating the plane of each step in a structure in an inspection area and outliers and inliers. [Figure 5] FIG. 10 is an explanatory diagram showing a process of converting a captured image into a normal image. [Figure 6] FIG. 10 is an explanatory diagram showing a process for detecting the position of a reinforcing bar in a normal image. [Figure 7] 7A, 7B, and 7C are explanatory diagrams showing the process of identifying line segments corresponding to reinforcing bar portions in a mask image. [Figure 8] FIG. 10 is an explanatory diagram showing a process of extracting a partial image of a reinforcing bar from a normalized image. [Figure 9] 9A and 9B are explanatory diagrams showing the process of determining the diameter of a reinforcing bar using a normalized image. [Figure 10] 10A and 10B are explanatory diagrams showing a process of calculating the correlation between a frequency conversion spectrum obtained from a normalized image and a reference spectrum. [Figure 11] FIG. 10 is an explanatory diagram showing a lap joint in a reinforcement inspection area. [Figure 12] 12A and 12B are block diagrams showing a hardware configuration for realizing the functions of the bar arrangement inspection device according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Embodiment 1 FIG. 1 is a block diagram showing the configuration of a reinforcement inspection device 1 according to a first embodiment. The reinforcement inspection device 1 inspects the reinforcing bars arranged in a reinforcement plane based on an image of the reinforcement plane to be inspected taken by a stereo camera 2, and displays the inspection results on a display unit 3. The reinforcement plane to be inspected is a plane included in an architectural or civil engineering structure constructed with a plurality of reinforcing bars arranged as a framework. For example, the reinforcement inspection device 1 determines the diameter (type; name) of the reinforcing bars in the reinforcement plane, and inspects the number of reinforcing bars of the determined diameter arranged and the spacing between adjacent reinforcing bars. A tablet terminal, smartphone, or personal computer (PC) is used for the reinforcement inspection device 1.

[0010] For example, there are 16 types of rebars based on the JIS standard. These 16 types of rebars are given "designations": D4, D5, D6, D8, D10, D13, D16, D19, D22, D25, D29, D32, D35, D38, D41, and D51. The designation indicates the rounded diameter of the rebar. For example, the nominal diameter of a rebar designated D10 is 9.53 mm, the nominal diameter of a rebar designated D13 is 12.7 mm, and the nominal diameter of a rebar designated D16 is 15.9 mm. Note that rebars generally used as the framework of buildings are D10 or higher.

[0011] Each of the 16 types of rebar has a specified "maximum average knot spacing." For example, the maximum average knot spacing for D10 rebars is 6.7 mm, the maximum average knot spacing for D13 rebars is 8.9 mm, and the maximum average knot spacing for D16 rebars is 11.1 mm. As such, only the maximum average knot spacing is specified for the 16 types of rebars, and the actual knot spacing for rebars varies depending on the manufacturer or production lot. Also, threaded rebars may have different diameters even if the knot spacing is the same.

[0012] The reinforcing bar inspection device 1 acquires scanning lines consisting of pixel values ​​of multiple pixels aligned along the longitudinal direction of the reinforcing bar in an image of the reinforcing bar plane captured by the stereo camera 2, for each pixel in a direction perpendicular to the longitudinal direction of the reinforcing bar, and calculates feature quantities by frequency-converting the pixel values ​​of the scanning lines.The reinforcing bar inspection device 1 then identifies the boundary position between the reinforcing bar and the background based on the result of comparing the calculated feature quantities with reference feature quantities, and identifies the reinforcing bar diameter using the boundary position.This reinforcing bar diameter is designated as the first reinforcing bar diameter.

[0013] Furthermore, the reinforcing bar arrangement inspection device 1 infers the reinforcing bar diameter using a learning model that outputs the reinforcing bar diameter when an image of the reinforcing bar is input. This reinforcing bar diameter is designated as the second reinforcing bar diameter. The reinforcing bar arrangement inspection device 1 determines the reinforcing bar diameter of the measurement result using the first reinforcing bar diameter and the second reinforcing bar diameter. In this way, the reinforcing bar inspection device 1 uses images of the reinforcing bars to compare the reinforcing bar diameters determined by the above two methods to determine the final measurement result of the reinforcing bar diameter, so it is possible to accurately determine the reinforcing bar diameter even for reinforcing bars with the same node spacing.

[0014] The stereo camera 2 is a photographing unit having a left photographing unit, a right photographing unit, and a three-dimensional information generating unit (not shown). The left photographing unit photographs a left viewpoint image seen from the left side. The right photographing unit photographs a right viewpoint image seen from the right side. The three-dimensional information generating unit generates a three-dimensional image as three-dimensional information by performing stereo matching processing on the left viewpoint image and the right viewpoint image. The display unit 3 is a display device provided in the reinforcement arrangement inspection device 1.

[0015] The display unit 3 is, for example, a liquid crystal display (LCD) or an organic electroluminescence (EL) display device. The memory unit 4 stores rebar characteristic information for various rebar diameters. The rebar characteristic information is the aforementioned reference characteristic amount, and is a reference frequency transformation spectrum obtained by frequency transforming pixel values ​​of a scanning line consisting of multiple pixels aligned along the longitudinal direction of the rebar. For example, the frequency transformation is a fast Fourier transform (hereinafter referred to as FFT).

[0016] The reinforcement inspection device 1 includes a three-dimensional information acquisition unit 11, a plane identification unit 12, an image conversion unit 13, a position detection unit 14, an image extraction unit 15, a feature calculation unit 16, an inference unit 17, a rebar diameter determination unit 18, and a measurement result information generation unit 19. The three-dimensional information acquisition unit 11 acquires, as three-dimensional information, three-dimensional images of the reinforcement plane to be inspected taken by the stereo camera 2.

[0017] The plane identifying unit 12 identifies the reinforcement plane to be inspected from the inspection area using the three-dimensional information acquired by the three-dimensional information acquiring unit 11. For example, the plane identifying unit 12 detects plane candidates containing multiple three-dimensional points from a three-dimensional image of a structure including a reinforcement plane, and calculates the number of three-dimensional points whose distance from the plane candidate is equal to or less than a threshold for each plane candidate. Then, the plane identifying unit 12 identifies the plane candidate with the largest number of three-dimensional points as the reinforcement plane to be inspected from among the multiple plane candidates. The reinforcement plane to be inspected is the plane located at the forefront of the structure.

[0018] The image converter 13 converts the captured image of the inspection area into a normal image that faces the reinforcement plane of the inspection target. The normal image is an image in which the distance between the stereo camera 2 and the reinforcement plane of the inspection target is constant and the reinforcement plane of the inspection target faces the stereo camera 2 directly. For example, the image conversion unit 13 estimates a homography transformation matrix using the position coordinates of four points at the four corners of a rectangle formed by reinforcing bars arranged in a lattice pattern on a reinforcement plane. Then, the image conversion unit 13 converts the captured image into a normal-oriented image based on the homography transformation matrix. All pixels in the orthogonal image are scaled so that the distance from the stereo camera 2 is constant. As a result, in the orthogonal image, differences in the size of the rebars according to the distance between the stereo camera 2 and the reinforcement plane of the inspection target are corrected.

[0019] The position detection unit 14 detects the position information of the rebar in the reinforcement plane of the inspection target using the oriented image generated by the image conversion unit 13. For example, the position detection unit 14 binarizes the oriented image using a threshold value to generate a mask image in which parts other than the rebar are masked, and detects the position of the rebar in the reinforcement plane of the inspection target contained in the oriented image by counting the pixels of the rebar part of the mask image.

[0020] The image extraction unit 15 extracts rebar images from the orientated image based on the rebar position information in the rebar arrangement plane of the inspection target. For example, the image extraction unit 15 identifies the rebar image for each rebar in the orientated image based on the rebar position information in the rebar arrangement plane of the inspection target, and sequentially extracts multiple partial images of the same size in the longitudinal direction of the rebar from the rebar image. The multiple partial images extracted by the image extraction unit 15 are output to the inference unit 17 for each rebar image.

[0021] The feature calculation unit 16 acquires a scanning line consisting of pixel values ​​of a plurality of pixels aligned along the longitudinal direction of the rebar in the normal image for each pixel in a direction perpendicular to the longitudinal direction of the rebar, and calculates a feature by frequency-converting the pixel values ​​of the scanning line using FFT.The feature calculation unit 16 then identifies the boundary position between the rebar and the background based on the result of comparing the calculated feature with a reference feature, and identifies the diameter of the first rebar using the boundary position.

[0022] The inference unit 17 infers the second rebar diameter using a learning model that outputs the rebar diameter when a rebar image is input. For example, the learning model is a machine learning model that infers the second rebar diameter when multiple partial images extracted by the image extraction unit 15 for each rebar image are input. The learning algorithm may be, for example, deep learning, neural networks, genetic programming, functional logic programming, or support vector machines.

[0023] The reinforcing bar diameter determination unit 18 determines the reinforcing bar diameter of the measurement result using the first reinforcing bar diameter and the second reinforcing bar diameter. For example, the reinforcing bar diameter determination unit 18 determines the reinforcing bar diameter of the first reinforcing bar diameter and the second reinforcing bar diameter that satisfies a predetermined determination condition as the reinforcing bar diameter of the measurement result. The determination condition is a condition that the reinforcing bar diameter that is the same as or closest to the reinforcing bar diameter specified in the JIS standard is determined to be the measurement result. The reinforcing bar diameter determination unit 18 may also determine the reinforcing bar diameter obtained by statistically processing the first reinforcing bar diameter and the second reinforcing bar diameter as the reinforcing bar diameter of the measurement result. The statistical processing may include, for example, calculating the average value, maximum value, or minimum value.

[0024] The measurement result information generating unit 19 generates and outputs measurement result information for displaying the rebar diameter of the measurement result. For example, the measurement result information generating unit 19 outputs display control information for displaying the rebar diameter of the measurement result to the display unit 3 as measurement result information. The display unit 3 displays the rebar diameter of the measurement result using the measurement result information.

[0025] FIG. 2 is a flowchart showing the reinforcement bar arrangement inspection method according to the first embodiment. The three-dimensional information acquisition unit 11 acquires three-dimensional information of the inspection area where reinforcing bars are arranged (step ST1). FIG. 3 is an explanatory diagram showing an overview of the photographing process of the reinforcing bar arrangement inspection area, and shows a three-dimensional image 2C of a plurality of reinforcing bar arrangement planes where reinforcing bars 20 are arranged. As shown in FIG. 3, the stereo camera 2 captures a left viewpoint image 2A and a right viewpoint image 2B, and then generates a three-dimensional image 2C using the left viewpoint image 2A and the right viewpoint image 2B. The three-dimensional information acquisition unit 11 acquires the three-dimensional image 2C from the stereo camera 2.

[0026] The plane identifying unit 12 identifies the reinforcement bar plane to be inspected from the multiple reinforcement bar planes in the inspection area using the three-dimensional image 2C (step ST2). Here, in the three-dimensional image 2C, the closer to the stereo camera 2, i.e., the closer to the front, the larger the reinforcing bar 20 appears, and the farther from the stereo camera 2, the smaller the reinforcing bar 20 appears. The fact that the reinforcing bar 20 appears larger means that there are more three-dimensional points corresponding to the reinforcing bar 20 in the three-dimensional image 2C. Conversely, the fact that the reinforcing bar 20 appears smaller means that there are fewer three-dimensional points corresponding to the reinforcing bar 20 in the three-dimensional image 2C. Furthermore, the closer to the front the plane is, the fewer obstacles, such as reinforcing bars on the rear side, that block the view from the stereo camera 2, making occlusion less likely to occur.

[0027] Therefore, the plane identification unit 12 identifies the foreground plane using, for example, the RANSAC (RANdom Sample Consensus) method. The plane identification unit 12 repeatedly estimates a function indicating plane candidates using a group of three-dimensional points randomly detected from the three-dimensional image 2C. Figures 4A, 4B, and 4C are graphs showing the relationship between functions P(1), P(2), and P(3) indicating the planes of each layer and outliers 31 and inliers 32, and show three-dimensional points on the XY coordinate plane. Outliers 31 are three-dimensional points that are not included in the allowable range, and inliers 32 are three-dimensional points that are included in the allowable range.

[0028] In the RANSAC method, the number of 3D points that are inliers 32 is counted for each parameter representing the functions P(1), P(2), and P(3), and the parameter with the highest count is determined to be the optimal parameter. That is, the plane candidate represented by the function to which the determined parameters are applied is determined to be the foreground plane estimation result. As is clear from Figures 4A, 4B, and 4C, the parameters representing the function P(3) have the largest number of 3D points that are inliers 32, so the plane identification unit 12 identifies the plane candidate represented by the function P(3) as the foreground plane.

[0029] Next, the image conversion unit 13 converts the image of the inspection area including the reinforcement plane of the inspection target, captured by the stereo camera 2, into a normalized image (step ST3). 5 is an explanatory diagram showing the process of converting a captured image into a normal-oriented image, and shows only the foreground reinforcement plane in the three-dimensional image 2C and the normal-oriented image 2D. The image conversion unit 13 specifies four corners of any rectangle among the reinforcing bars 20 arranged in a grid pattern on the reinforcement plane to be inspected in the three-dimensional image 2C, and estimates a homography transformation matrix that gives the rectangle a shape viewed from the front of the stereo camera 2. Then, based on the homography transformation matrix, the image conversion unit 13 converts the image shown in the three-dimensional image 2C into the normal-oriented image 2D.

[0030] The position detection unit 14 uses the orientated image to detect rebar position information in the reinforcement bar arrangement plane (step ST4-1). FIG. 6 is an explanatory diagram showing the process of detecting the position of rebar in the orientated image. As shown in FIG. 6, the position detection unit 14 converts the orientated image 2D converted from the three-dimensional image 2C by the image conversion unit 13 into a mask image 51. The mask image 51 is an image with binary pixel values ​​in which areas showing rebar in the orientated image 2D are represented by "1" and other areas are represented by "0". Areas with a pixel value of "1" are areas of white pixels, and areas with a pixel value of "0" are areas of black pixels.

[0031] For example, the position detection unit 14 uses the left viewpoint image 2A and the right viewpoint image 2B to calculate the amount of misalignment between the images of rebars at the same position as a left-right disparity, identifies the image portion of the rebar using the disparity, and generates a mask image 51 by assigning a pixel value of "1" to the identified image portion and a pixel value of "0" to the other image portions. The position detection unit 14 then detects, as the position of the rebar, a position in the mask image 51 where the count of white pixels is equal to or greater than a threshold. The position detection unit 14 can detect the position of the rebar in the X direction and the Y direction by rotating the mask image 51 and counting the number of white pixels. That is, the positions of the rebars lined up vertically and the rebars lined up horizontally are detected in the orthogonal image 2D.

[0032] Next, the position detection unit 14 performs a process of identifying line segments corresponding to rebars in the mask image 51. FIGS. 7A, 7B, and 7C are explanatory diagrams showing the process of identifying line segments corresponding to rebar portions in the mask image. As shown in FIG. 7A, when the mask image 51 includes white pixel regions A, B, and C, it is unclear which region corresponds to the image of the rebar. Therefore, the position detection unit 14 calculates the length D(1) of region A, the length D(2) of region B, and the length D(3) of region C on the coordinate axis passing through the white pixel regions A, B, and C, the distance D(4) between region A and region B, and the distance D(5) between region B and region C.

[0033] As shown in FIG. 7B , the position detection unit 14 determines that areas with a distance equal to or greater than the threshold, between the distances D(4) and D(5), are not rebars, and determines that areas with a distance less than the threshold are image areas corresponding to the same rebar. For example, the position detection unit 14 determines that the image area indicated by the line segment 52 with a length D(6) that passes through area A and area B, where the distance D(4) is less than the threshold, is an image area corresponding to the same rebar. Furthermore, because D(5) is equal to or greater than the threshold, the position detection unit 14 determines that there is no image area corresponding to a rebar between area B and area C in the mask image 51. The position detection unit 14 assigns position information of the portion of the mask image 51 that is determined to be the image area of ​​a rebar to the orthogonalized image 2D and outputs the orthogonalized image 2D to the image extraction unit 15 and the feature calculation unit 16.

[0034] The image extraction unit 15 extracts a rebar image from the frontalized image based on the rebar position information in the rebar layout plane of the inspection target (step ST4-2). FIG. 8 is an explanatory diagram showing the process of extracting a partial image of the rebar 20 from the frontalized image. As shown in FIG. 8, the image extraction unit 15 extracts the frontalized image 2E of the rebar 20 along the longitudinal direction from the frontalized image. Further, the image extraction unit 15 sequentially extracts partial images 2F(1), 2F(2), and 2F(3) of the same size from the frontalized image 2E and outputs them to the inference unit 17. The partial images 2F(1), 2F(2), and 2F(3) are, for example, square images with the same number of pixels in the vertical and horizontal directions. The image extraction unit 15 extracts partial images for all the rebars 20 in the frontalized image 2D.

[0035] The feature amount calculation unit 16 identifies the boundary position between the rebar and the background based on the result of comparing the feature amount calculated from the frontalized image with the reference feature amount, and identifies the first rebar diameter using the identified boundary position (step ST5-1). FIGS. 9A and 9B are explanatory diagrams showing the process of determining the rebar diameter using the frontalized image. FIG. 9A shows a scan line i (i = 0 to n, 0 < k < n) composed of the luminance values of a plurality of pixels arranged along the longitudinal direction of the rebar in the frontalized image 2E. As shown in FIG. 9A, the feature amount calculation unit 16 acquires the scan line i for each pixel in the direction orthogonal to the longitudinal direction of the rebar, and calculates a feature amount (frequency conversion spectrum) obtained by frequency-converting the luminance value of the scan line i by FFT for each pixel in the direction orthogonal to the longitudinal direction of the rebar.

[0036] FIG. 9B shows the change amount of the luminance value of the scan line i = 0 in the background portion outside the rebar in the frontalized image 2E and the frequency conversion spectrum obtained by frequency-converting the luminance value of the scan line i = 0, the change amount of the luminance value of the scan line i = k on the rebar and the frequency conversion spectrum obtained by frequency-converting the luminance value of the scan line i = k, the change amount of the luminance value of the scan line i = n in the background portion outside the rebar and the frequency conversion spectrum obtained by frequency-converting the luminance value of the scan line i = n, and the reference frequency conversion spectrum which is the rebar feature information. When there is a change in the magnitude of the luminance value in the scan line i, the change in the luminance value becomes a peak in the frequency conversion spectrum.

[0037] In the orthogonal image 2E, the feature amount calculation unit 16 acquires scanning lines i for each pixel from scanning lines i=0 to i=n in the portion corresponding to the outside of the rebar in a direction perpendicular to the longitudinal direction of the rebar (from left to right in FIG. 9A). Next, the feature amount calculation unit 16 calculates a frequency conversion spectrum by frequency-converting the luminance value of scanning line i for each pixel. Subsequently, the feature amount calculation unit 16 compares the frequency conversion spectrum by frequency-converting the luminance value of scanning line i with the rebar characteristic information stored in the storage unit 4. The rebar characteristic information is a frequency spectrum by frequency-converting the luminance values ​​of scanning lines on the rebar that have been calculated in advance in the rebar image.

[0038] For example, the feature amount calculation unit 16 determines that a spectrum similar to the frequency conversion spectrum, which is the rebar characteristic information, among the frequency conversion spectra calculated for each pixel in the orientated image 2E corresponds to the scanning line i on the rebar. Next, the feature amount calculation unit 16 identifies the scanning line i corresponding to the boundary position between the rebar and the background (the boundary on the left side and the boundary on the right side in FIG. 9A) and calculates the distance between the identified scanning lines i. Thereafter, the feature amount calculation unit 16 calculates the first rebar diameter by converting the distance between the scanning lines i into a length in real space. For example, if the stereo camera 2 is orientated with respect to the rebar plane at a shooting distance of 1.5 meters, and the captured image has a horizontal resolution of 200 opx, this corresponds to 0.75 mm / px in real space.

[0039] The reinforcing bar characteristic information may include a frequency transform spectrum of the luminance values ​​of scanning line i calculated using a captured image in which the reinforcing bar and its shadow are prominent due to the influence of sunlight, or may include a frequency transform spectrum of the luminance values ​​of scanning line i calculated using an image in which the reinforcing bar is shining white due to flash photography, etc. By using such reinforcing bar characteristic information, the feature amount calculation unit 16 can accurately identify the boundary between the reinforcing bar and the background even in a reinforcing bar image in which there is a shadow or the white glow of the reinforcing bar.

[0040] The start point for scanning the rebar image and the end point for ending the scan are determined by, for example, setting a scanning range that is the maximum rebar diameter specified in the JIS standard plus a certain margin, and setting the rebar image within that scanning range. This results in one side of the scanning range being the start point and the other side being the end point. The start point for scanning the rebar image and the end point for ending the scan may also be set by the user using an input device (not shown).

[0041] Fig. 10 is an explanatory diagram showing the process of calculating the correlation between a frequency-transformed spectrum obtained from a normalized image and a reference spectrum. In Fig. 10, the solid-line spectrum is the reference frequency-transformed spectrum, which is rebar characteristic information, and the dashed-line spectrum is the frequency-transformed spectrum obtained from the normalized image. For example, the feature calculation unit 16 performs a correlation calculation (1) that evaluates the correlation between these spectra over the entire frequency range, a correlation calculation (2) that evaluates the correlation between first peaks that appear first in the spectra, and a correlation calculation (3) that evaluates the correlation between second peaks that appear next in the spectra.

[0042] In correlation calculation (1), the feature amount calculation unit 16 calculates a correlation score (1) indicating the correlation between both spectra within a correlation evaluation range expressed as (min(PeakA, PeakB)-4) to 256, where PeakA is the intensity of the first peak that first appears in the spectrum that is the rebar characteristic information, and PeakB is the intensity of the first peak that first appears in the spectrum obtained from the orientated image. In correlation calculation (2), the feature amount calculation unit 16 calculates a correlation score (2) indicating the correlation between both spectra within a correlation evaluation range expressed as PeakA±10. In correlation calculation (3), the feature amount calculation unit 16 calculates a correlation score (3) indicating the correlation between both spectra within a correlation evaluation range expressed as (PeakA×2)±10.

[0043] The feature amount calculation unit 16 determines a weighting coefficient according to each of the correlation score (1), correlation score (2), and correlation score (3). Then, the feature amount calculation unit 16 multiplies the determined weighting coefficient by the correlation score (1), correlation score (2), and correlation score (3), and then adds them together to obtain an overall correlation score. The feature amount calculation unit 16 compares the overall correlation score with a threshold, and if the overall correlation score is equal to or greater than the threshold, determines that the spectrum obtained from the orientated image is similar to the rebar characteristic information.

[0044] Furthermore, the feature calculation unit 16 masks the portion of the scanning line i where a rebar parallel to the scanning line i intersects with another rebar, and calculates the feature by frequency-converting the pixel values ​​of the masked scanning line i. In the reinforcement plane, the rebars are arranged in a grid pattern, and there are portions where the rebars intersect with each other. In these portions, other rebars are arranged in a direction perpendicular to the longitudinal direction of the rebar along the scanning line i, making it difficult to accurately identify the boundary between the rebar and the background. Therefore, the feature calculation unit 16 masks the portion where the rebar parallel to the scanning line i intersects with another rebar, and calculates the feature by frequency-converting the pixel values ​​of the masked scanning line i. This allows the feature calculation unit 16 to accurately identify the boundary between the rebar and the background.

[0045] Furthermore, the feature calculation unit 16 may divide the scan line i into multiple sections along the longitudinal direction of the rebar, identify the boundary position between the rebar and the background based on the result of comparing the feature calculated for each section with a reference feature, and detect a lap splice made of multiple rebars and the length of the lap splice based on the number of rebars having a first rebar diameter identified using the boundary position. FIG. 11 is an explanatory diagram showing a lap splice in the reinforcing bar arrangement inspection area. In FIG. 11, rebar 20A and rebar 20B are lap splices. The feature calculation unit 16 divides the scan line i into multiple sections along the longitudinal direction of the rebar. The memory unit 4 stores rebar feature information including a frequency conversion spectrum of the brightness values ​​of the scan line corresponding to the spaces between the rebars in the lap splice.

[0046] For example, the area where reinforcing bar 20A intersects with another reinforcing bar is set as the feature acquisition interval, and the feature is calculated for each interval to calculate the first reinforcing bar diameter. At the lap joint between reinforcing bar 20A and reinforcing bar 20B, the frequency transform spectrum of the luminance values ​​of scan line i at the boundary between reinforcing bar 20A and reinforcing bar 20B has a waveform different from that at the boundary between the reinforcing bar and the background. In FIG. 11, scanning is started pixel by pixel from the reinforcing bar 20A side, and a frequency transform spectrum corresponding to the boundary between reinforcing bar 20A and the background is acquired, followed by a frequency transform spectrum corresponding to the reinforcing bar 20A itself. Further scanning results in a frequency transform spectrum corresponding to the boundary between reinforcing bar 20A and reinforcing bar 20B, followed by a frequency transform spectrum corresponding to the boundary between reinforcing bar 20B and the background. By comparing these frequency conversion spectra with the reinforcing bar characteristic information, the characteristic amount calculation unit 16 can determine that the reinforcing bar 20A is lap-jointed with the reinforcing bar 20B. Furthermore, by determining the diameter of the first reinforcing bar for each of the above sections, the feature calculation unit 16 can identify the position where the reinforcing bar 20B begins to be detected and the position where only the reinforcing bar 20B begins to be detected, and therefore it is also possible to determine the length of the lap joint based on the identified positions.

[0047] 2, when an image of a rebar extracted from the normalized image 2D is input, the inference unit 17 infers a second rebar diameter using a learning model that outputs the rebar diameter (step ST5-2). The learning model is generated using learning data that is, for example, a set of multiple partial images extracted from the rebar image and correct labels that indicate the rebar diameter assigned to each partial image. The inference unit 17 performs inference for each partial image using the learning model, and calculates, for each partial image, the probability that a rebar with the second rebar diameter D is captured and the probability that something other than a rebar is captured.

[0048] The learning model calculates an inference result for the rebar image by averaging the inference results for all partial images of the same rebar image. For example, the inference result includes an average value of the probability that each type of rebar appears in the image and an average value of the probability that something other than a rebar appears in the image. The inference unit 17 determines the second rebar diameter based on this inference result. The learning model may be generated using training data including partial images of rebars in which the rebars and their shadows are large due to the influence of sunlight, and partial images in which the rebars are shining white due to flash photography, etc. By generating a learning model using such training data, the learning model can recognize the boundary between the rebars and the background even when there is a shadow or the white light of the rebars, and can accurately infer the diameter of the second rebar.

[0049] Next, the rebar diameter determination unit 18 determines the rebar diameter of the measurement result using the first rebar diameter and the second rebar diameter (step ST6). For example, the rebar diameter determination unit 18 determines the rebar diameter of the measurement result from the first rebar diameter and the second rebar diameter that satisfies a predetermined determination condition. If the determination condition is to determine that the measurement result is a value that is the same as or closest to the rebar diameter specified in the JIS standard, the rebar diameter determination unit 18 compares the first rebar diameter and the second rebar diameter with the rebar diameter specified in the JIS standard, and if either or both of the first rebar diameter and the second rebar diameter are the same as or closest to the rebar diameter specified in the JIS standard, determines that the rebar diameter is the measurement result. This makes it possible to accurately determine the rebar diameter.

[0050] The reinforcing bar diameter determination unit 18 may also determine the reinforcing bar diameter obtained by statistically processing the first reinforcing bar diameter and the second reinforcing bar diameter as the reinforcing bar diameter of the measurement result. Statistical processing may include, for example, calculating the average value, maximum value, or minimum value. The reinforcing bar diameter determination unit 18 determines the average value of the first reinforcing bar diameter and the second reinforcing bar diameter, or the maximum or minimum value of the first reinforcing bar diameter and the second reinforcing bar diameter, as the measurement result. Furthermore, the reinforcing bar diameter determination unit 18 may determine the reinforcing bar diameter obtained by statistically processing the first reinforcing bar diameter and the second reinforcing bar diameter as the determination result if the diameter satisfies the above-mentioned determination condition. This method also makes it possible to accurately determine the reinforcing bar diameter.

[0051] The measurement result information generating unit 19 generates and outputs measurement result information for displaying the rebar diameter of the measurement result (step ST7). For example, the measurement result information generating unit 19 outputs display control information for displaying the rebar diameter of the measurement result on an electronic whiteboard to the display unit 3 as measurement result information. The display unit 3 displays the rebar diameter of the measurement result on the electronic whiteboard, which is screen information. By referring to the rebar diameter of the measurement result displayed on the display unit 3, the inspector can appropriately check the rebar diameter of the measurement result.

[0052] The functions of the three-dimensional information acquisition unit 11, plane identification unit 12, image conversion unit 13, position detection unit 14, image extraction unit 15, feature calculation unit 16, inference unit 17, reinforcing bar diameter determination unit 18, and measurement result information generation unit 19 provided in the reinforcement bar inspection device 1 are realized by a processing circuit. That is, the reinforcement bar inspection device 1 includes a processing circuit for executing the processes of steps ST1 to ST7 shown in Fig. 2. The processing circuit may be dedicated hardware, or may be a CPU (Central Processing Unit) that executes a program stored in memory.

[0053] Fig. 12A is a block diagram showing a hardware configuration that realizes the functions of the bar arrangement inspection device 1. Fig. 12B is a block diagram showing a hardware configuration that executes software that realizes the functions of the bar arrangement inspection device 1. In Figs. 12A and 12B, the input interface 100 is an interface that relays three-dimensional data output from the stereo camera 2 to the bar arrangement inspection device 1. The output interface 101 is an interface that relays the inspection results and the like that are output from the bar arrangement inspection device 1 to the display unit 3.

[0054] When the processing circuit is the dedicated hardware processing circuit 102 shown in FIG. 12A, the processing circuit 102 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof. The functions of the three-dimensional information acquisition unit 11, plane identification unit 12, image conversion unit 13, position detection unit 14, image extraction unit 15, feature calculation unit 16, inference unit 17, rebar diameter determination unit 18 and measurement result information generation unit 19 provided in the reinforcement inspection device 1 may be realized by separate processing circuits, or these functions may be realized together by a single processing circuit.

[0055] 12B, the functions of the three-dimensional information acquisition unit 11, plane identification unit 12, image conversion unit 13, position detection unit 14, image extraction unit 15, feature calculation unit 16, inference unit 17, reinforcing bar diameter determination unit 18, and measurement result information generation unit 19 provided in the reinforcement bar arrangement inspection device 1 are realized by software, firmware, or a combination of software and firmware. The software or firmware is written as a program and stored in the memory 104.

[0056] The processor 103 reads out and executes the programs stored in the memory 104, thereby realizing the functions of the three-dimensional information acquisition unit 11, plane identification unit 12, image conversion unit 13, position detection unit 14, image extraction unit 15, feature calculation unit 16, inference unit 17, reinforcing bar diameter determination unit 18, and measurement result information generation unit 19 provided in the reinforcement bar inspection device 1. For example, the reinforcement bar inspection device 1 includes the memory 104 for storing a program that, when executed by the processor 103, results in the execution of the processes from step ST1 to step ST7 shown in FIG. These programs cause the computer to execute the procedures or methods of processing performed by the three-dimensional information acquisition unit 11, plane identification unit 12, image conversion unit 13, position detection unit 14, image extraction unit 15, feature calculation unit 16, inference unit 17, rebar diameter determination unit 18, and measurement result information generation unit 19. The memory 104 may be a computer-readable storage medium that stores programs for causing the computer to function as the three-dimensional information acquisition unit 11, plane identification unit 12, image conversion unit 13, position detection unit 14, image extraction unit 15, feature calculation unit 16, inference unit 17, rebar diameter determination unit 18, and measurement result information generation unit 19.

[0057] Memory 104 may be, for example, a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically-EPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD, etc.

[0058] Some of the functions of the three-dimensional information acquisition unit 11, plane identification unit 12, image conversion unit 13, position detection unit 14, image extraction unit 15, feature calculation unit 16, inference unit 17, rebar diameter determination unit 18 and measurement result information generation unit 19 provided in the reinforcement inspection device 1 may be realized by dedicated hardware, and other functions may be realized by software or firmware. For example, the three-dimensional information acquisition unit 11 realizes its functions by a processing circuit 102, which is dedicated hardware, and the plane identification unit 12, image conversion unit 13, position detection unit 14, image extraction unit 15, feature calculation unit 16, inference unit 17, rebar diameter determination unit 18, and measurement result information generation unit 19 realize their functions by a processor 103 reading and executing programs stored in a memory 104. In this way, the processing circuit can realize the above functions by hardware, software, firmware, or a combination of these.

[0059] The reinforcement inspection device 1 may include a learning device that generates a learning model used by the inference unit 17. The reinforcement inspection device 1 may also acquire a learning model stored in an external device by data communication via a network. Furthermore, the learning model may use, as learning data, partial images to which correct labels have been assigned that have been input using an operation unit (not shown in FIG. 1). This enables the inference unit 17 to infer the rebar diameter using a learning model generated for each site, thereby improving the accuracy of reinforcement inspection.

[0060] As described above, the reinforcing bar inspection device 1 according to the first embodiment acquires a scanning line consisting of pixel values ​​of multiple pixels aligned along the longitudinal direction of the reinforcing bar, for each pixel in the direction perpendicular to the longitudinal direction of the reinforcing bar, calculates feature values ​​by frequency-converting the pixel values ​​of the scanning line, identifies the boundary position between the reinforcing bar and the background based on the results of comparing the calculated feature values ​​with reference feature values, identifies the diameter of the first reinforcing bar using the boundary position, infers the diameter of the second reinforcing bar using a learning model, and determines the diameter of the reinforcing bar from the measurement results using the diameter of the first reinforcing bar and the diameter of the second reinforcing bar. This allows the reinforcing bar inspection device 1 to determine the diameter of reinforcing bars even if they have the same node spacing.

[0061] In the bar arrangement inspection device 1 according to the first embodiment, the feature calculation unit 16 acquires a scanning line consisting of the brightness values ​​of a plurality of pixels aligned along the longitudinal direction of the rebar in the orthogonal image for each pixel in the direction perpendicular to the longitudinal direction of the rebar, and calculates a feature by frequency-converting the brightness values ​​of the scanning line. This enables the bar arrangement inspection device 1 to accurately measure the diameter of the first rebar using the orthogonal image.

[0062] In the bar arrangement inspection device 1 according to the first embodiment, the feature calculation unit 16 masks the portion of the scan line where a reinforcing bar parallel to the scan line intersects with another reinforcing bar, and calculates the feature by frequency-converting the pixel values ​​of the masked scan line. By masking the portion where the reinforcing bars intersect, the feature calculation unit 16 can accurately measure the diameter of the first reinforcing bar using the orientated image.

[0063] In the reinforcement bar arrangement inspection device 1 according to the first embodiment, the feature calculation unit 16 divides the scanning line into multiple sections along the longitudinal direction of the rebar, identifies the boundary position between the rebar and the background based on the result of comparing the feature calculated for each section with a reference feature, and detects lap splices made of multiple rebars and the lengths of the lap splices based on the number of rebars having the first rebar diameter identified using the boundary position. This allows the feature calculation unit 16 to detect lap splices made of rebars and the lengths of the lap splices.

[0064] In the bar arrangement inspection device 1 according to the first embodiment, the reinforcing bar diameter determination unit 18 determines the reinforcing bar diameter of the measurement result as the reinforcing bar diameter of the first reinforcing bar diameter and the second reinforcing bar diameter that satisfies a predetermined determination condition. This allows the reinforcing bar diameter determination unit 18 to accurately determine the reinforcing bar diameter of the measurement result using the first reinforcing bar diameter actually measured from the captured image of the inspection area and the second reinforcing bar diameter inferred by the learning model.

[0065] In the bar arrangement inspection device 1 according to the first embodiment, the reinforcing bar diameter determination unit 18 determines the reinforcing bar diameter obtained by statistically processing the first reinforcing bar diameter and the second reinforcing bar diameter as the reinforcing bar diameter of the measurement result. This allows the reinforcing bar diameter determination unit 18 to accurately determine the reinforcing bar diameter of the measurement result by using the first reinforcing bar diameter actually measured from the captured image of the inspection area and the second reinforcing bar diameter inferred by the learning model.

[0066] In the reinforcement inspection method according to the first embodiment, a three-dimensional information acquisition unit 11 acquires three-dimensional information of an inspection area in which reinforcing bars are arranged, a plane identification unit 12 uses the three-dimensional information to identify the reinforcement plane of the inspection target from the inspection area, an image conversion unit 13 converts the captured image of the inspection area into an orthogonal image that is oriented squarely to the reinforcement plane of the inspection target, a position detection unit 14 detects reinforcing bar position information in the reinforcement plane using the orthogonal image, an image extraction unit 15 extracts reinforcing bar images from the orthogonal image based on the reinforcing bar position information in the reinforcement plane of the inspection target, and a feature calculation unit 16 calculates the feature amount of the reinforcing bar images arranged along the longitudinal direction of the reinforcing bars in the orthogonal image. A scanning line consisting of pixel values ​​of multiple pixels is acquired for each pixel in a direction perpendicular to the longitudinal direction of the rebar, feature values ​​are calculated by frequency-converting the pixel values ​​of the scanning line, the calculated feature values ​​are compared with reference feature values ​​to identify the boundary position between the rebar and the background based on the results, the boundary position is used to identify the first rebar diameter, an inference unit 17 infers the second rebar diameter using a learning model that outputs the rebar diameter when a rebar image is input, a rebar diameter determination unit 18 determines the rebar diameter of the measurement result using the first rebar diameter and the second rebar diameter, and a measurement result information generation unit 19 generates and outputs measurement result information indicating the rebar diameter of the measurement result. This provides a rebar arrangement inspection method that can determine the rebar diameter even for rebars with the same node spacing.

[0067] Any of the components of the embodiments may be modified or omitted.

[0068] Aspects of the present disclosure are described in the appendix below. (Appendix 1) a three-dimensional information acquisition unit that acquires three-dimensional information of an inspection area in which reinforcing bars are arranged; a plane specifying unit that specifies a reinforcement plane to be inspected from the inspection area using the three-dimensional information; an image conversion unit that converts the captured image of the inspection area into a normal image that faces the reinforcement plane of the inspection target; a position detection unit that detects reinforcing bar position information in the reinforcement bar arrangement plane using the orthogonal image; a feature calculation unit that acquires a scanning line consisting of pixel values ​​of a plurality of pixels aligned along the longitudinal direction of the rebar in the orthogonal image for each pixel in a direction perpendicular to the longitudinal direction of the rebar, calculates feature values ​​by frequency-converting the pixel values ​​of the scanning line, identifies a boundary position between the rebar and the background based on the result of comparing the calculated feature values ​​with a reference feature value, and identifies a first rebar diameter using the boundary position; an image extraction unit that extracts a reinforcing bar image from the orthogonal image based on the reinforcing bar position information in the reinforcing bar arrangement plane of the inspection target; an inference unit that infers a second rebar diameter using a learning model that outputs a rebar diameter when the rebar image is input; a reinforcing bar diameter determination unit that determines the reinforcing bar diameter of the measurement result using the first reinforcing bar diameter and the second reinforcing bar diameter; a measurement result information generating unit that generates and outputs measurement result information indicating the reinforcing bar diameter of the measurement result. This is a reinforcement inspection device characterized by the following. (Appendix 2) The feature amount calculation unit acquires a scanning line consisting of brightness values ​​of a plurality of pixels aligned along the longitudinal direction of the rebar in the orthogonal image for each pixel in a direction perpendicular to the longitudinal direction of the rebar, and calculates a feature amount by frequency-converting the brightness values ​​of the scanning line. 2. The reinforcement bar inspection device according to claim 1, (Appendix 3) The feature amount calculation unit masks a portion of the scanning line where a reinforcing bar parallel to the scanning line intersects with another reinforcing bar, and calculates a feature amount by frequency-converting pixel values ​​of the masked scanning line. 3. The bar arrangement inspection device according to claim 1 or 2, (Appendix 4) The feature amount calculation unit divides the scanning line into a plurality of sections along the longitudinal direction of the reinforcing bars, identifies a boundary position between the reinforcing bars and the background based on the result of comparing the feature amount calculated for each section with a reference feature amount, and detects lap joints made of a plurality of reinforcing bars and the length of the lap joints based on the number of reinforcing bars having the first reinforcing bar diameter identified using the boundary position. 4. The bar arrangement inspection device according to claim 1, wherein: (Appendix 5) The reinforcing bar diameter determination unit determines the reinforcing bar diameter that satisfies a predetermined determination condition from among the first reinforcing bar diameter and the second reinforcing bar diameter as the reinforcing bar diameter of the measurement result. 5. The bar arrangement inspection device according to claim 1, wherein: (Appendix 6) The reinforcing bar diameter determination unit determines the reinforcing bar diameter obtained by statistically processing the first reinforcing bar diameter and the second reinforcing bar diameter as the reinforcing bar diameter of the measurement result. 5. The bar arrangement inspection device according to claim 1, wherein: (Appendix 7) A reinforcement inspection method using a reinforcement inspection device, a step in which a three-dimensional information acquisition unit acquires three-dimensional information of an inspection area in which reinforcing bars are arranged; a step in which a plane specifying unit specifies a reinforcement plane to be inspected from the inspection area using the three-dimensional information; an image conversion unit converting the captured image of the inspection area into a normal image that faces the reinforcement plane of the inspection target; A step in which a position detection unit detects reinforcing bar position information in the reinforcement bar arrangement plane using the orthogonal image; An image extraction unit extracts a reinforcing bar image from the orthogonal image based on the reinforcing bar position information in the reinforcing bar arrangement plane of the inspection target; a feature calculation unit acquires a scanning line consisting of pixel values ​​of a plurality of pixels aligned along the longitudinal direction of the rebar in the orthogonal image for each pixel in a direction perpendicular to the longitudinal direction of the rebar, calculates a feature by frequency-converting the pixel values ​​of the scanning line, identifies a boundary position between the rebar and the background based on the result of comparing the calculated feature with a reference feature, and identifies a first rebar diameter using the boundary position; an inference unit inferring a second reinforcing bar diameter using a learning model that outputs a reinforcing bar diameter when the reinforcing bar image is input; A step in which a reinforcing bar diameter determination unit determines the reinforcing bar diameter of the measurement result using the first reinforcing bar diameter and the second reinforcing bar diameter; a step in which a measurement result information generating unit generates and outputs measurement result information indicating the reinforcing bar diameter of the measurement result. A reinforcement inspection method characterized by the above. (Appendix 8) A program for causing a computer to function as the reinforcement inspection device according to any one of appendices 1 to 6. [Explanation of symbols]

[0069] 1 Reinforcement inspection device, 2 Stereo camera, 2A Left viewpoint image, 2B Right viewpoint image, 2C Three-dimensional image, 2D, 2E Oriented images, 2F(1) to 2F(3) Partial images, 3 Display unit, 4 Memory unit, 11 Three-dimensional information acquisition unit, 12 Plane identification unit, 13 Image conversion unit, 14 Position detection unit, 15 Image extraction unit, 16 Feature calculation unit, 17 Inference unit, 18 Reinforcement diameter determination unit, 19 Measurement result information generation unit, 20, 20A, 20B Reinforcement bars, 31 Outlier, 32 Inlier, 51 Mask image, 52 Line segment, 100 Input interface, 101 Output interface, 102 Processing circuit, 103 Processor, 104 Memory.

Claims

1. a three-dimensional information acquisition unit that acquires three-dimensional information of an inspection area in which reinforcing bars are arranged; a plane specifying unit that specifies a reinforcement plane to be inspected from the inspection area using the three-dimensional information; an image conversion unit that converts the captured image of the inspection area into a normal image that faces the reinforcement plane of the inspection target; a position detection unit that detects reinforcing bar position information in the reinforcement bar arrangement plane using the orthogonal image; an image extraction unit that extracts a reinforcing bar image from the orthogonal image based on the reinforcing bar position information in the reinforcing bar arrangement plane of the inspection target; a feature calculation unit that acquires a scanning line consisting of pixel values ​​of a plurality of pixels aligned along the longitudinal direction of the rebar in the orthogonal image for each pixel in a direction perpendicular to the longitudinal direction of the rebar, calculates feature values ​​by frequency-converting the pixel values ​​of the scanning line, identifies a boundary position between the rebar and the background based on the result of comparing the calculated feature values ​​with a reference feature value, and identifies a first rebar diameter using the boundary position; an inference unit that infers a second reinforcing bar diameter using a learning model that outputs a reinforcing bar diameter when the reinforcing bar image is input; a reinforcing bar diameter determination unit that determines the reinforcing bar diameter of the measurement result using the first reinforcing bar diameter and the second reinforcing bar diameter; a measurement result information generating unit that generates and outputs measurement result information for displaying the reinforcing bar diameter of the measurement result. This is a reinforcement inspection device characterized by the following.

2. The feature amount calculation unit acquires the scanning line, which is made up of brightness values ​​of a plurality of pixels arranged along the longitudinal direction of the reinforcing bar in the orthogonal image, for each pixel in a direction perpendicular to the longitudinal direction of the reinforcing bar, and calculates feature amounts by frequency-converting the brightness values ​​of the scanning line.

2. The reinforcing bar inspection device according to claim 1.

3. The feature amount calculation unit masks a portion of the scanning line where a reinforcing bar parallel to the scanning line intersects with another reinforcing bar, and calculates a feature amount by frequency-converting pixel values ​​of the masked scanning line.

3. The reinforcing bar inspection device according to claim 1 or 2.

4. The feature amount calculation unit divides the scanning line into a plurality of sections along the longitudinal direction of the reinforcing bars, identifies the boundary position between the reinforcing bars and the background based on the result of comparing the feature amount calculated for each section with a reference feature amount, and detects lap joints made of a plurality of reinforcing bars and the length of the lap joints based on the number of reinforcing bars having the first reinforcing bar diameter identified using the boundary position.

4. The reinforcing bar inspection device according to claim 3.

5. The reinforcing bar diameter determination unit determines the reinforcing bar diameter that satisfies a predetermined determination condition from among the first reinforcing bar diameter and the second reinforcing bar diameter as the reinforcing bar diameter of the measurement result.

2. The reinforcing bar inspection device according to claim 1.

6. The reinforcing bar diameter determination unit determines the reinforcing bar diameter obtained by statistically processing the first reinforcing bar diameter and the second reinforcing bar diameter as the reinforcing bar diameter of the measurement result.

2. The reinforcing bar inspection device according to claim 1.

7. A reinforcement inspection method using a reinforcement inspection device, a step in which a three-dimensional information acquisition unit acquires three-dimensional information of an inspection area in which reinforcing bars are arranged; a step in which a plane specifying unit specifies a reinforcement plane to be inspected from the inspection area using the three-dimensional information; an image conversion unit converting the captured image of the inspection area into a normal image that faces the reinforcement plane of the inspection target; A step in which a position detection unit detects reinforcing bar position information in the reinforcement bar arrangement plane using the orthogonal image; An image extraction unit extracts a reinforcing bar image from the orthogonal image based on the reinforcing bar position information in the reinforcing bar arrangement plane of the inspection target; a feature calculation unit acquires a scanning line consisting of pixel values ​​of a plurality of pixels aligned along the longitudinal direction of the rebar in the orthogonal image for each pixel in a direction perpendicular to the longitudinal direction of the rebar, calculates a feature by frequency-converting the pixel values ​​of the scanning line, identifies a boundary position between the rebar and the background based on the result of comparing the calculated feature with a reference feature, and identifies a first rebar diameter using the boundary position; an inference unit inferring a second reinforcing bar diameter using a learning model that outputs a reinforcing bar diameter when the reinforcing bar image is input; A step in which a reinforcing bar diameter determination unit determines the reinforcing bar diameter of the measurement result using the first reinforcing bar diameter and the second reinforcing bar diameter; a step in which a measurement result information generating unit generates and outputs measurement result information for displaying the reinforcing bar diameter of the measurement result. A reinforcement inspection method characterized by the above.

8. A program for causing a computer to function as the bar arrangement inspection device according to claim 1.

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