Fixture spacing estimation device

Abstract

To provide a screw pitch estimation device capable of correctly estimating the pitch of screws hammered into a plasterboard.SOLUTION: An estimation device 10 estimates the screw pitch of a plurality of screws 6, 6, ... for joining rectangular plasterboards 5. The estimation device 10 includes an estimation line extraction part 11 for extracting estimation lines PA, PB, ... of the plasterboards 5 from extracted images of the plasterboards 5, a screw group classification part 12 for classifying images of the plurality of screws 6, 6, ... into groups of images of the screws 6, 6, ... hammered along the estimation lines PA, PB of adjacent plasterboards 5, 5 on the basis of the estimation lines PA, PB, a boundary line estimation part 13 for calculating an approximate line NA that divides the screws into two groups along a boundary of the adjacent plasterboards 5, 5, and estimating the approximate line NA to be a boundary line BA of the plasterboards 5, 5, and an interval calculation part 16 for calculating an interval Pv fo the adjacent screws 6, 6 along the boundary line BA for each plasterboard.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an estimation device that estimates the distance between adjacent fasteners among a plurality of fasteners driven into a rectangular plate material. [Background technology]

[0002] Conventionally, as shown in Patent Document 1, board materials such as gypsum boards used for interior wall materials or ceiling materials are attached to a substrate via a plurality of fasteners such as screws. The plurality of fasteners are driven into the board at specified intervals along at least the periphery of the board. After the board is installed, it is inspected to see if the spacing between the fasteners is within the specified intervals. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-031241 Summary of the Invention [Problem to be solved by the invention]

[0004] While such inspections can be performed visually, it is also conceivable to photograph the plate material into which the fasteners have been driven and measure the spacing between the fasteners from the photographed image. However, for example, when plate materials are adjacent to each other, it is difficult to use image processing or the like to identify the fasteners that have been driven along the boundary between the plate materials for each plate from the photographed image of the plate materials and then estimate the spacing between adjacent fasteners.

[0005] The present invention has been made in consideration of the above points, and its object is to provide a fastener spacing estimation device that can easily and accurately estimate the spacing between fasteners driven into a plate material. [Means for solving the problem]

[0006] In consideration of the above-mentioned problems, a fastener spacing estimation device according to the present invention is an estimation device that estimates the spacing between adjacent fasteners among a plurality of fasteners driven into each of a plurality of rectangular plate materials, and is characterized by comprising at least an estimated line extraction unit that extracts an image of the plate material and an image of each fastener from an overall image including the plate material captured by an imaging device, and extracts an estimated line of the outline of the plate material from the extracted image of the plate material; a fastener group classification unit that classifies the images of the plurality of fasteners into groups of images of fasteners driven along the estimated line of adjacent plate materials based on the estimated line; a boundary line estimation unit that sets coordinates of the classified plurality of fasteners with respect to the overall image, calculates an approximate line for dividing each group of the classified plurality of fasteners into two groups along the boundary between the adjacent plate materials based on the set coordinates, and estimates that the calculated approximate line is the boundary line of the plate materials at the boundary between the adjacent plate materials; and a spacing calculation unit that calculates the spacing between adjacent fasteners along the boundary line for each of the plate materials.

[0007] According to the present invention, the estimated line extraction unit extracts an image of the plate and an image of each fastener from an overall image including the plate captured by the imaging device, and extracts an estimated line of the plate's outline from the extracted image of the plate. Examples of this extraction include extraction using machine learning to extract the image of the plate, and extraction using image processing such as edge detection.

[0008] The fastener group classification unit can classify images into groups of fasteners driven along the estimated line estimated by the estimated line extraction unit. The classified group of fastener images is a group of images of fasteners driven along the boundary between adjacent plate materials, and this group includes groups of images of fasteners driven into two plate materials. The boundary line estimation unit then sets coordinates of the classified fasteners relative to the overall image and calculates an approximation line that divides each group of classified fasteners into two groups along the boundary between adjacent plate materials based on the set coordinates. This approximation line can be estimated to be the boundary line between the adjacent plate materials. As a result, the spacing calculation unit can easily and accurately calculate the spacing between adjacent fasteners in a group of fasteners that exist on either side of the estimated boundary line.

[0009] Here, in the extraction by the estimated line extraction unit, a single contour line (boundary line) along the boundary between adjacent plate materials may be extracted as a plurality of different intermittent estimated lines. In such a case, the interval between the fasteners is calculated for each of the plurality of different intermittent estimated lines, and it is therefore possible that the calculated interval between the fasteners may differ slightly from the actual interval between the fasteners. From this perspective, in a more preferred embodiment, the fastener group classification unit groups the estimated lines extracted by the estimated line extraction unit into estimated lines corresponding to the boundaries between the adjacent plate materials by cluster analysis using coordinates of pixels constituting the estimated lines, and classifies the images of the fasteners into groups based on the grouped estimated lines.

[0010] However, according to this aspect, the estimated lines extracted by the estimated line extraction unit are grouped into estimated lines that face the boundaries of adjacent plate materials by cluster analysis using the coordinates of the pixels that make up the estimated lines, and the images of the fasteners are classified into groups based on these grouped estimated lines, so that the groups of images of fasteners that lie along the boundary lines can be classified more accurately.

[0011] Here, among the fasteners driven into the plate material, there are fasteners that are driven along a scribed line on the center line of the plate material. Such scribed lines are also mistaken for estimated lines, and even if the fastener group classification unit classifies the images of the fasteners into groups based on these estimated lines and the boundary line estimation unit estimates a boundary line, this boundary line is not a boundary line but the center line of the plate material. Therefore, from this perspective, in a more preferred embodiment, the boundary line estimation unit calculates the distance between the coordinates of the classified multiple fasteners and the approximation line, and estimates whether the approximation line is the boundary line based on the calculated distance.

[0012] According to this aspect, it is possible to accurately estimate whether the calculated boundary line is a boundary line between the plate materials. For example, if the distance between the coordinates of the classified multiple fasteners and the approximation line is close, for example, if the distance is such that an image of the fastener is on the approximation line, it can be estimated that the approximation line is not a boundary line between the plate materials but is some other line such as a scribe line.

[0013] Here, there is no particular problem if the overall image is captured from a position directly facing the plate, but if, for example, the image is captured from an angle oblique to the plate, there is a risk that the spacing between the fasteners cannot be accurately calculated. In view of this, the estimation device further includes a contour line setting unit that sets, for each plate, a contour line of the plate that includes the boundary line estimated by the boundary line estimation unit, and a projection transformation unit that projectively transforms images of the plate and the multiple fasteners driven into the plate into a front image viewed from the front, based on the contour line set by the contour line setting unit, and the spacing calculation unit calculates the spacing between the fasteners for each plate with respect to the front image. [Effects of the Invention]

[0014] According to the present invention, the spacing between fasteners driven into a plate material can be easily and accurately estimated. [Brief explanation of the drawings]

[0015] [Figure 1]FIG. 1 is a schematic diagram of a screw pitch estimation device according to an embodiment of the present invention. [Figure 2] FIG. 2 is a control block diagram of a calculation unit of the estimation device shown in FIG. [Figure 3] FIG. 3 is a control block diagram of a projective transformation unit shown in FIG. 2. [Figure 4] 3 is a schematic diagram for explaining extraction of an estimated line of the outline of a gypsum board by the estimated line extraction unit shown in FIG. 2. FIG. [Figure 5] 3 is a schematic diagram for explaining classification of a group of images of screws driven along the boundary of a plasterboard by the screw group classification unit shown in FIG. 2. FIG. [Figure 6] 3(a) to 3(c) are schematic diagrams for explaining classification of a group of images of screws driven into the periphery of a plasterboard by the boundary line estimation unit shown in FIG. 2. [Figure 7] FIG. 3 is a schematic diagram for explaining a contour line setting unit shown in FIG. 2. [Figure 8] FIG. 10 is a schematic diagram for explaining a projective transformation unit. [Figure 9] FIG. 2 is an estimation flow diagram using the estimation device shown in FIG. DETAILED DESCRIPTION OF THE INVENTION

[0016] An estimation device 10 according to this embodiment will be described below with reference to FIGS. 1. About boards and fixtures In this embodiment, the estimation device 10 is a device that estimates the spacing between adjacent fasteners among a plurality of fasteners driven into a rectangular board material. Here, the fasteners are driven at predetermined intervals at least along the periphery of the board material. Examples of combinations of board material and fasteners include eaves soffit material (decorative material under the eaves) and nails that secure it, structural plywood in wooden houses and nails that secure it, and floor underlayment and nails / screws that secure it.

[0017] In the following embodiment, gypsum board 5 is exemplified as the plate material, and screws 6 are exemplified as the fasteners. Therefore, gypsum board 5 corresponds to the "plate material" of the present invention, screws 6 correspond to the "fasteners" of the present invention, and the pitch of screws 6 (screw pitch) described below corresponds to the "spacing of fasteners" of the present invention.

[0018] The fasteners for fixing the gypsum board 5 may be nails or staples. The gypsum board 5 is used as an interior wall material or ceiling material, etc. The gypsum board 5 is fixed with screws 6 along the long and short sides of the gypsum board 5 at a pitch equal to or less than that specified by laws and regulations. The screw pitch here refers to the distance between adjacent screws 6, 6 along the long and short sides of the gypsum board 5.

[0019] Here, the multiple screws 6, 6, ... are driven into the gypsum board 5 at a specified pitch along the periphery of the gypsum board 5, inside the periphery. Furthermore, in this embodiment, the multiple screws 6, 6, ... are driven into the gypsum board 5 at a specified pitch in the center of the width direction of the gypsum board 5 along the long side direction of the gypsum board 5. After the gypsum board 5 is installed, the screw pitch is checked to see if it is within the specified pitch. The screw pitch is the center-to-center distance between adjacent screws 6 along the long side direction and the short side direction when the gypsum board 5 is viewed from the front.

[0020] The screw pitch estimation device 10 according to this embodiment is a device that estimates the pitch (screw pitch) of screws 6 driven into a gypsum board 5 after the gypsum board 5 has been installed. The estimation device 10 ultimately extracts images of the screws 6 from an image G1 including the gypsum board 5 captured by an imaging device 20, and estimates the pitch of adjacent screws 6.

[0021] 2. Hardware configuration of the estimation device 10 The estimation device 10 is composed of hardware such as ROM and RAM, and is equipped with a memory unit 10A in which the conditions of the gypsum board 5, a screw pitch estimation program, etc. are recorded, and a calculation unit 10B that executes the screw pitch estimation program.

[0022] An input device 31 and an output device 32 are connected to the estimation device 10. In this embodiment, the input device 31 and the output device 32 may be integrated into a touch panel display. Data such as the specifications of the gypsum board 5 and a screw pitch estimation program is input to the input device 31. In this embodiment, image data captured by the imaging device 20 is input to the input device 31. The data input by the input device 31 is stored in the memory unit 10A. The output device 32 displays the image data captured by the imaging device 20, the calculation results calculated by the calculation unit 10B, etc.

[0023] In this embodiment, the estimation device 10 is configured with a storage unit 10A and a calculation unit 10B, but may also include, for example, an input device 31 and an output device 32. The estimation device 10 may further include an imaging device 20 in addition to the input device 31 and the output device 32, and may be a mobile terminal such as a smartphone or tablet that integrates these.

[0024] 3. Software configuration of the estimation device 10 In this embodiment, as shown in FIG. 2, the estimation device 10 includes at least an estimated line extraction unit 11, a screw group classification unit (fixture group classification unit) 12, a boundary line estimation unit 13, a contour line setting unit 14, a projective transformation unit 15, and a pitch calculation unit 16.

[0025] 3-1. Estimated Line Extraction Unit 11 As shown in Figure 4, the estimated line extraction unit 11 extracts an image of the gypsum board 5 and images of each screw (fixing device) 6 from the overall image G1 including the board material captured by the imaging device 20, and extracts estimated lines PA, PB, EA to ED of the outline of the gypsum board 5 from the extracted image of the gypsum board 5.

[0026] The estimated line extraction unit 11 may use, for example, a support vector machine (SVM) or the like to learn the feature amounts of the gypsum board 5 from the captured image using an image of the gypsum board 5 captured by the imaging device 20 and feature amounts of the shape of the gypsum board 5 in this image (for example, multiple points along the edge of the gypsum board) as training data. This makes it possible to extract an image of the gypsum board from any overall image G1 that includes an image of the gypsum board 5. Alternatively, after identifying the gypsum board itself using a cascade classifier that uses feature amounts such as Haar-like feature amounts, the image of the gypsum board 5 may be extracted from the image of the identification range using the machine learning results described above.

[0027] The positions of multiple screws 6 relative to the overall image G1 are identified using a similar method. For example, in this embodiment, it is sufficient to be able to calculate the screw pitch, and ultimately, it is sufficient to identify the center positions of the screws 6. Therefore, since it is not necessary to accurately identify the image size of the screws 6 or the shape of the screws 6, the screws 6 may be identified using a cascade classifier or the like that has machine learning knowledge of the screw shapes. Once the positions of the screws 6 are identified, as a result, as shown in FIG. 8, the screws 6 can be identified from the front image of the gypsum board 5 after projective transformation. For example, a circular mark indicating the identified screw 6 may be added around the screw 6. This mark may be a square, diamond, or other shape other than a circle. Alternatively, the transformed front image may be binarized or grayscale processed, and the screws 6 may be identified based on the number of pixels that have a predetermined brightness difference from the surrounding pixels.

[0028] In addition, since the gypsum board 5 is rectangular, the contour line consisting of the four sides of the gypsum board 5 may be detected as an estimated line using edge detection (such as edge detection using the Canny method, etc.) and commonly known line detection (such as line detection using the Hough method, etc.).

[0029] 3-2. About the Screw Group Classification Section (Fixture Group Classification Section) 12 Based on the estimated lines PA and PB shown in Figure 4, the screw group classification unit 12 classifies a group of images of multiple screws 6 into groups 6A and 6B of images of screws 6 driven along the estimated lines PA and PB of adjacent gypsum boards 5, as shown in Figure 5.

[0030] Here, during extraction by the estimated line extraction unit 11, a single contour line (boundary line) along the boundary between adjacent gypsum boards 5, 5 may be extracted as multiple, different, intermittent estimated lines. More specifically, in FIG. 4, an estimated line PA (PB) is extracted as a single line for each boundary line between adjacent gypsum boards 5, 5. However, as shown in FIG. 5, the estimated lines PA, PB may not each be extracted as a single line, but may be extracted as multiple intermittent estimated lines PAa, PAb (PBa, PBb) along the vertical direction in FIG. 5. In such cases, the spacing between adjacent screws 6, 6 is calculated for each estimated line PAa, PAb (PBa, PBb), and it is expected that the calculated spacing between the screws 6, 6 will be slightly different from the actual spacing between the screws. From this perspective, the screw group classification unit 12 groups (classifies) the estimated lines according to the following content before classifying the screw groups.

[0031] Specifically, the screw group classification unit 12 performs cluster analysis using the coordinates of the pixels that make up the estimated lines PAa, PAb, PBa, and PBb (the XY Cartesian coordinates of each pixel relative to the entire image G1, which will be described later), to group (classify) the estimated lines PAa, PAb, PBa, and PBb extracted by the estimated line extraction unit 11 into estimated lines that correspond to the boundaries (boundary lines) between adjacent gypsum boards 5, 5. In this embodiment, the cluster analysis results in grouping (classification) into a group of estimated lines PAa and PAb and a group of estimated lines PBa and PBb.

[0032] More specifically, the screw group classification unit 12 performs cluster analysis using a Bayesian Gaussian mixture model or the like on the coordinate values ​​of the estimated lines PAa, PAb, PBa, and PBb of the gypsum board 5, and groups the estimated lines PAa, PAb, PBa, and PBb into a group of estimated lines PAa and PAb and a group of estimated lines PBa and PBb. Note that, as shown in Figure 4, when estimated lines PA and PB are appropriately extracted as estimated lines corresponding to the boundaries (boundary lines) of adjacent gypsum boards 5, they are classified (grouped) into estimated lines PA and estimated line PB by this cluster analysis.

[0033] The number of clusters is also estimated according to the distribution of coordinate values ​​of each pixel of the estimated lines PAa, PAb, PBa, and PBb extracted by the estimated line extraction unit 11. Here, only clusters whose height (difference between the minimum and maximum coordinate values ​​in the Y direction) estimated from the estimated lines PAa, PAb, PBa, and PBb is equal to or greater than 1 / 2 to 1 / 3 of the height (length in the long side direction) of the entire image Gl or the gypsum board 5 are extracted.

[0034] The screw group classification unit 12 classifies images of multiple screws 6 into groups 6A and 6B of images of screws 6 driven along the estimated lines of adjacent gypsum boards 5, 5 based on the grouped estimated lines PAa, PAb and the grouped estimated lines PBa, PBb. Specifically, as shown in FIG. 5, for the grouped estimated lines PAa, PAb, images of screws 6 within a range (area) located a predetermined distance from both sides of the estimated lines PAa, PAb are estimated to be group 6A of images of screws 6. A similar method is used to estimate group 6B of images of screws 6 for the grouped estimated lines PBa, PBb. Note that in the case of the estimated lines PA, PB shown in FIG. 4, images of screws 6 located within a predetermined distance from both sides of the estimated lines PA, PB are estimated to be groups 6A and 6B.

[0035] The groups 6A and 6B of images of screws 6 classified here are groups of images of screws driven along the boundary between adjacent gypsum boards 5, and these groups include images of both screws 6 driven into the two gypsum boards 5. Therefore, in this embodiment, the boundary line estimation unit 13 divides these screws 6 into two.

[0036] 3-3. Boundary line estimation unit 13 As shown in Figures 6(a) and 6(b), the boundary line estimation unit 13 sets the coordinates (position coordinates) of the classified multiple screws 6, 6, ... (groups 6A, 6B) relative to the overall image G1, and based on the set coordinates, calculates approximate lines NA, NB that divide each group 6A, 6B of the classified multiple screws 6, 6, ... into two groups along the boundary between adjacent gypsum boards 5.

[0037] Specifically, as shown in FIG. 6(b), a Cartesian coordinate system (XY coordinate system) is set for the image of the gypsum board 5, and the equation of a line through which each screw 6, 6 passes is determined using the least squares method or the like for the center coordinates (center position coordinates) of each screw 6 in the image groups 6A, 6B of the screw 6. The line of this equation is designated as the approximate line NA, NB. The center coordinate of the screw 6 is determined by the coordinate of the pixel corresponding to the center of the pixels constituting the image of the screw 6. Note that principal component analysis may be performed on the center coordinates of each of the image groups 6A, 6B of the screw 6, and the approximate line NA, NB may be determined by the line (principal component axis) passing through the center of the screw group and in the direction of the first principal component. The boundary line estimation unit 13 estimates the calculated approximate line NA, NB to be the boundary line BA, BB of the gypsum board 5 at the boundary between adjacent gypsum boards 5.

[0038] Here, some of the screws 6 driven into the gypsum board 5 are driven along a scribed line drawn on the center line of the gypsum board 5. Such scribed lines may also be mistaken for an estimated line by the estimated line extraction unit 11. Even if the screw group classification unit 12 classifies the images of the screws 6 into groups based on these estimated lines and the boundary line estimation unit 13 estimates a boundary line, this boundary line is not a boundary line but the center line of the gypsum board 5. Therefore, from this point of view, the boundary line estimation unit 13 may calculate the distance between the coordinates of the classified multiple screws 6 and the approximation line N, and estimate whether the approximation line N is a boundary line based on the calculated distance.

[0039] 6(c), if the coordinates of the classified multiple screws 6 are close to the approximation line N, for example, if the distance is such that an image of the screw 6 is on the approximation line N, it is estimated that the approximation line N is not the boundary line BA, BB between the gypsum boards 5, but is some other line such as a scribe line. In this way, the contour line setting unit 14 can accurately estimate whether the calculated approximation lines NA, NB are the boundary lines BA, BB of the gypsum boards 5.

[0040] 3-4. About the contour setting section 14 The contour line setting unit 14 sets, for each gypsum board 5, a contour line (specifically, the straight lines of the four sides) of the gypsum board 5 including the boundary lines BA and BB estimated by the boundary line estimation unit 13. Here, as shown in Fig. 4, the contour line of each gypsum board 5 may be set using the overall contour lines (estimated lines) EA, EB, EC, and ED of the multiple gypsum boards 5 estimated by the estimated line extraction unit 11. Approximate straight lines NEA to NED may be created for the contour lines EA to ED by the least squares method, principal component analysis, or the like based on the coordinates of the pixels that make up the contour line (coordinates in an XY Cartesian coordinate system), and contour lines c1 to c4 of the gypsum board 5 may be set using these approximate straight lines.

[0041] In this case, similar to the process shown in the screw group classification unit 12, the contour lines EA to ED may be grouped (classified) by cluster analysis using the coordinates of the pixels of the contour lines EA to ED extracted by the estimated line extraction unit 11, and for each of the grouped contour lines EA to ED, the above-mentioned approximate lines NEA to NED may be created for the coordinates of the pixels constituting each contour line, and these approximate lines may be used to set the contour lines c1 to c4 of the gypsum board 5. In this way, even if at least one of the contour lines EA to ED extracted by the estimated line extraction unit 11 is an intermittent line, it is possible to calculate a more accurate approximate line NEA to NED by grouping it.

[0042] Alternatively, cluster analysis may be performed to classify groups 6C, 6D, 6E, and 6F of images of screws 6 based on the contour lines EA, EB, EC, and ED. As with the approximation lines described above, for the groups 6C, 6D, 6E, and 6F of images of screws 6 classified in this manner, approximation lines NEA to NED passing through the planar coordinates (coordinates in an XY Cartesian coordinate system) of multiple screws 6 belonging to that group may be calculated using the least squares method or the like, and these calculated approximation lines NEA to NED may be offset by a predetermined number of pixels outside the gypsum board 5 and set as the contour lines of the gypsum board 5. In this way, contour lines c1 to c4 of the gypsum board 5 can be set as shown in FIG. 7.

[0043] Here, there is no particular problem if the entire image G1 is captured from a position directly facing the plasterboard 5. However, if the entire image G1 is captured from an angle oblique to the plasterboard 5, the image of the plasterboard 5 will look like the left image in FIG. 8. Therefore, in this case, there is a risk that the screw spacing cannot be accurately calculated. For this reason, the estimation device 10 may be provided with a projective transformation unit 15.

[0044] 3-5. Projection transformation unit 15 The projection transformation unit 15 performs projection transformation on the gypsum board 5 and the screws 6 driven into it into a front image G3 viewed from the front. The method of projection transformation by the projection transformation unit 15 is not particularly limited as long as it can perform projection transformation on the extracted image G2 of the gypsum board 5 into a front image G3 viewed from the front.

[0045] For example, the lengths of the short and long sides of a rectangular gypsum board 5 may be input, and the corresponding contour lines c1 to c4 of the image G1 of the gypsum board 5 may be determined to be the short or long sides. The extracted image G2 of the gypsum board 5 may then be converted into a front image G3 so that the aspect ratio of the contour lines c1 to c4 of the image G1 of the gypsum board 5 matches the ratio of the short side to the long side. In this case, the intersection points g1 to g4 of the contour lines c1 to c4 are calculated, and the lengths of the contour lines c1 to c4 are calculated from the coordinates of these intersection points. Note that in the projective transformation shown in FIG. 8, expansion and contraction processing of the contour pixels may be performed. The method of converting into the front image G3 is a commonly known method, and detailed description thereof will be omitted.

[0046] 3-6. Pitch calculation unit (interval calculation unit) 16 The pitch calculation unit 16 calculates the screw pitch P between adjacent screws 6, 6 along the vertical direction (lengthwise direction) of the front image G3 and the screw pitch P between adjacent screws 6, 6 along the horizontal direction (lengthwise direction) of the front image G3 based on the positions of the plurality of screws 6, 6 relative to the projection-transformed front image G3. The pitch calculation unit 16 corresponds to the "spacing calculation unit" in the present invention.

[0047] Specifically, the distance between the centers of the screws 6 is calculated as the number of pixels. If the length of the long or short side of the gypsum board 5 is input in advance, the actual length and width of each pixel can be calculated from these long and short sides, and the screw pitch P can be calculated from these actual lengths. Alternatively, if the entire image G1 is acquired by the imaging device 20 together with a scale of the actual lengths or a mark corresponding to this, the actual length and width of each pixel can be calculated.

[0048] The pitch calculation unit 16 determines whether the calculated screw pitch P is within a specified range. Specifically, if the measured screw pitch is within the specified range, each screw is surrounded by a solid circle, and if it is not within the specified range, it is surrounded by a dashed circle.

[0049] According to this embodiment, the image G2 of the gypsum board 5 extracted by the estimated line extraction unit 11 is projectively transformed by the projection transformation unit into a front image G3 viewed from the front. As a result, even when the gypsum board 5 is imaged from an oblique direction, the image G2 of the gypsum board 5 is transformed into the front image G3 together with the screws 6, and therefore the position (position coordinates) of the screws 6 in the front image G3 of the gypsum board 5 after the projection transformation can also be identified from the position (position coordinates) of the screws 6 before the projection transformation.

[0050] Rather than directly identifying the positions of the multiple screws 6, 6, ... from the projection-transformed front image G3, the positions of the screws 6 are identified from the image G2 of the gypsum board 5 before projection transformation, in which the image of the screws 6 is easy to identify, and then the positions of the multiple screws 6, 6, ... in the projection-transformed front image G3 can be identified based on those positions. As a result, the pitch calculation unit 16 can accurately calculate the spacing between the screws 6, 6 regardless of the imaging conditions.

[0051] An estimation flow diagram using the estimation device 10 will be described below with reference to FIG. First, in step S1, the imaging device 20 captures an image of an area including the gypsum board 5 as an inspection area, and acquires an entire image G1.

[0052] Next, in step S2, the estimated line extraction unit 11 extracts an image of the gypsum board 5 and an image of the screws 6 from the overall image G1 including the gypsum board 5 captured by the imaging device 20, and extracts an estimated line of the outline of the gypsum board 5 from the extracted image of the gypsum board 5. Here, the position of the screws 6 relative to the overall image G is identified.

[0053] Next, in step S3, the screw group classification unit 12 classifies groups of images of screws 6 driven along the estimated lines PA and PB into groups 6A and 6B of images of screws 6. In step S4, the boundary line estimation unit 13 sets the coordinates of the classified screws 6, 6, ... relative to the overall image G1, and calculates approximate lines NA and NB that divide each group of classified screws 6, 6, ... into two groups along the boundaries of adjacent gypsum boards 5 based on the set coordinates. In step S6, the boundary line estimation unit 13 estimates whether the above-mentioned approximate lines NA and NB are boundary lines BA and BB. In step S7, the image G2 of the gypsum board 5 is projectively transformed into a front image G3.

[0054] Next, in step S8, the positions (center positions) of the multiple screws 6 are identified from the front image G3 of the gypsum board 5, and in step S9, the pitch calculation unit 16 calculates the screw pitch P of adjacent screws from the image of the identified screws 6. Finally, in step S10, the pitch calculation unit 16 determines whether the screw pitch is within a specified range.

[0055] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to the above-described embodiments, and various design modifications can be made without departing from the spirit of the present invention as set forth in the claims.

[0056] In this embodiment, the screw group classification unit groups the estimated lines using cluster analysis based on the coordinates of the estimated lines, but for example, it may also classify a group of images of multiple screws into a group of images of screws driven along the estimated lines of adjacent gypsum boards using cluster analysis based on the coordinates of the estimated lines. [Explanation of symbols]

[0057] 5: plasterboard (board material), 6: screws (fixtures), 10: estimation device, 11: estimation line extraction unit, 12: fixture group classification unit (screw group classification unit), 13: boundary line estimation unit, 14: contour line setting unit, 15: projective transformation unit, 16: pitch calculation unit (spacing calculation unit), 20: imaging device, G1: overall image, G2: plasterboard image, G3: front image

Claims

1. An estimation device that estimates the spacing between adjacent fasteners among a plurality of fasteners driven into a plurality of rectangular plate materials, an estimated line extraction unit that extracts an image of the plate and an image of each fixing device from an overall image including the plate captured by an imaging device, and extracts an estimated line of the outline of the plate from the extracted image of the plate; a fastener group classification unit that classifies the images of the plurality of fasteners into groups of images of fasteners driven along the estimated line of adjacent plate materials based on the estimated line; a boundary line estimation unit that sets coordinates of the classified plurality of fasteners relative to the entire image, calculates an approximation line that divides each group of the classified plurality of fasteners into two groups along the boundary between the adjacent plate materials based on the set coordinates, and estimates that the calculated approximation line is the boundary line of the plate materials at the boundary between the adjacent plate materials; a spacing calculation unit that calculates the spacing between the adjacent fasteners along the boundary line for each of the plate materials; A fastener spacing estimation device comprising at least:

2. 2. The fastener spacing estimation device according to claim 1, wherein the fastener group classification unit groups the estimated lines extracted by the estimated line extraction unit into estimated lines corresponding to boundaries between the adjacent plate materials by cluster analysis using coordinates of pixels that make up the estimated lines, and classifies the images of the fasteners into groups based on the grouped estimated lines.

3. 3. The fastener spacing estimation device according to claim 1, wherein the boundary line estimation unit calculates a distance between the coordinates of the classified plurality of fasteners and the approximation line, and estimates whether the approximation line is the boundary line based on the calculated distance.

4. The estimation device includes: a contour line setting unit that sets, for each of the plate materials, a contour line of the plate material that includes the boundary line estimated by the boundary line estimation unit; a projection transformation unit that performs projection transformation on an image of the plate material and the plurality of fasteners driven into the plate material based on the contour line set by the contour line setting unit, into a front image viewed from the front, 4. The fastener spacing estimation device according to claim 1, wherein the spacing calculation unit calculates the spacing between the fasteners for each of the plate materials in the front image.

Images