Counting methods, counting systems, counting programs

Abstract

To provide technology for accurately counting an object included in a laminate.SOLUTION: A counting method acquires image data on a lamination surface of a laminate, partitions the image data into a plurality of predetermined regions with respect to a lamination direction and a width direction, generates a signal profile indicating each thickness component in the predetermined region, acquires a thickness component estimated from each predetermined region, and counts an object, on the basis of the thickness component.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method for counting objects included in a laminate, a counting system, and a counting program.

Background Art

[0002] Plate-like or sheet-like articles are stored in a stacked state. Conventionally, a technique for counting such articles by imaging them in a stacked state is known.

[0003] Patent Document 1 discloses a technique capable of simply and accurately counting the number of layers of a laminated pattern on various objects having a surface of the laminated pattern. In Patent Document 1, a step of obtaining a captured image 11 which is an image of the surface of a counting object 10 having a surface of a laminated pattern, a step of extracting a counting target area 11A which is a partial area of the captured image 11, a step of specifying a reference point within the counting target area 11A, and a step of determining an analysis area 15 based on the reference point and counting the number of layers of the laminated pattern based on the analysis area 15 are disclosed.

[0004] Patent Document 2 discloses a technique capable of easily and highly accurately counting substrates. In Patent Document 2, a step of obtaining an image of an end face of a plurality of substrates stacked in one direction, a step of calculating an integrated value by integrating the luminance of each pixel constituting this image for each pixel column in the other direction orthogonal to this one direction in this image, a step of detecting a substrate in the image based on this integrated value, and a step of counting the number of detected substrates to obtain a count value are disclosed. In Patent Document 2, it is described that by integrating the luminance of each pixel for each pixel column in the horizontal direction (X'-axis direction) of the image to obtain an integrated value (horizontal projection value), and further obtaining an edge projection value, the thickness of the substrate, the boundary position of the substrate, and the center position of the substrate can be calculated.

[0005] Patent Document 3 discloses a technology for measuring the number of laminated plywood sheets that automates the process of measuring the number of laminated plywood sheets and improves the accuracy of the measurement of the number of laminated sheets more efficiently and economically than conventional methods. Patent Document 3 discloses a laminated plywood sheet counting device 10 and a method for measuring the number of laminated plywood sheets that can measure the number of laminated sheets when multiple plywood sheets 11, which are made by bonding together multiple types of thin sheet materials 12 and 13 of different colors, are laminated together. The device discloses that an imaging means 15 images the side of the plywood sheet 11, and an information processing means 17 uses the brightness values ​​obtained from the image of the laminated plywood sheet 11 captured by the imaging means 15 to determine the types of thin sheet materials 12 and 13 that make up each plywood sheet 11 and calculate the number of laminated plywood sheets 11. Patent Document 3 discloses that, as image data processing of the surface of a laminate, various image processing is performed to emphasize the boundaries of the laminate, and processing is performed to calculate the distance between plate-like bodies and the reference pitch, the reference pitch is a value obtained by adding a predetermined tolerance to the thickness of the substrate, candidate points P are assigned to identify each plate-like body by referring to the reference pitch calculated here, and the candidate point P determined to be the starting position is set as a confirmed point P', and the number of plate-like bodies is counted by processing multiple candidate points consecutive to it as confirmed points.

[0006] Patent Document 4 discloses a method in which multiple folded sheets of paper 1 are aligned, light is shone on them to create bright areas 5 on the back 2 and shadow areas 6 in the recesses between the backs, and the images are captured by a television camera 7. The obtained image signal is converted into a multi-level color intensity signal for each color by an analog-to-digital conversion circuit 9. The color intensity signal of each component point constituting one horizontal scan line is compared with two calculated reference curved lines and the color intensity of each component point. If a component point exceeds the lower reference value, does not exceed the lower reference value downwards, exceeds the upper reference value upwards, and then exceeds the lower reference value downwards, it is added as one non-shadow area. This comparison is repeated until the other end, and the number of non-shadow areas is accumulated to count the number of non-shadow areas on the back of the paper, i.e., the number of sheets of paper.

[0007] Patent Document 5 discloses a plate-like body counting device for laminated plate-like bodies that calculates a reference pitch based on the thickness of the glass plate G, calculates candidate points equal to the reference pitch as confirmed points, and counts all of the calculated confirmed points. In other words, it recognizes the presence of glass plates G in an image received by a line sensor 48 and counts the number of glass plates G.

[0008] Patent Document 6 discloses a stacked disc counting device in which a two-dimensional image sensor camera 12 outputs images of the sides of stacked compact discs 2 as two-dimensional image data, and an image processing device 14 calculates the number of compact discs from this two-dimensional image data. [Prior art documents] [Patent Documents]

[0009] [Patent Document 1] Japanese Patent Publication No. 2021-071921 [Patent Document 2] Japanese Patent Publication No. 2015-228094 [Patent Document 3] Japanese Patent Publication No. 2018-036950 [Patent Document 4] Japanese Patent Publication No. 2014-002701 [Patent Document 5] Japanese Patent Publication No. 2014-032431 [Patent Document 6] Japanese Patent Application Publication No. 05-197851 [Overview of the Initiative] [Problems that the invention aims to solve]

[0010] The image recognition-based counting of plate-like or sheet-like objects contained within a laminate had room for improvement in terms of accuracy in determining the precise number of objects. In particular, objects made of materials such as steel plates were difficult to count accurately because they deform in-plane (in the width direction), creating gaps between the objects.

[0011] In view of the above-mentioned problems, the present invention aims to solve the problem of providing a technology for accurately counting objects contained in a laminate. [Means for solving the problem]

[0012] To solve the above-mentioned problems, the present invention provides a method for counting objects contained in a laminate, wherein a computer performs an image acquisition step, a generation step, a thickness estimation step, and a counting step, the image acquisition step acquires image data of the laminated surface of the laminate, the generation step divides the image data into a plurality of predetermined regions in the lamination direction and width direction and generates a signal profile showing the thickness component of each predetermined region, the thickness estimation step acquires the thickness component estimated from each of the predetermined regions, and the counting step counts the objects based on the thickness component.

[0013] By using this configuration, the counting is based on thickness components estimated from predetermined regions in the stacking direction and width direction of the stacked surface, thereby enabling accurate counting of objects that correspond to thicknesses that vary from region to region.

[0014] In a preferred embodiment of the present invention, the thickness estimation step generates a classification result of signal peaks extracted based on the thickness components included in the signal profile, and the counting step counts the objects based on the thickness components corresponding to each of the signal peaks in the classification result. This configuration allows for the classification of signal peaks in a way that increases the probability that each signal peak is a true thickness component. As a result, accurate counting can be performed using the thickness component corresponding to the optimally classified signal peak.

[0015] In a preferred embodiment of the present invention, the thickness estimation step determines a signal peak which is a valid value or an intensity peak which is an invalid value included in the signal profile in the classification result, according to the intensity of a thickness component greater than a predetermined value, and if a predetermined number or more boundary lines of the predetermined region are detected based on the thickness component in the classification result, the invalid value is determined to be a valid value, and the counting step counts the object based on the thickness component indicated by the signal profile determined to be a valid value. By adopting this configuration, accurate counting can be performed using the optimal thickness component by selecting appropriate significant values ​​included in the classification results.

[0016] In a preferred embodiment of the present invention, the invention includes a model estimation step of calculating model parameters using input values ​​that include thickness components of each predetermined region, and estimating a counting model having said parameters, wherein the counting step counts the objects based on the thickness components estimated by the counting model. By using this configuration, it is possible to achieve counting that references the optimal thickness component by employing a counting model that reflects each thickness component of a predetermined region.

[0017] In a preferred embodiment of the present invention, the model estimation step calculates model parameters using input values ​​that include thickness components for each predetermined region, estimates a plurality of counting models having these parameters and corresponding to each classification result, and determines the optimal counting model from the plurality of counting models corresponding to the classification result based on the difference between the actual values ​​of the thickness components for each predetermined region and the calculated values ​​of the thickness components by the counting model, and the counting step counts the objects based on the thickness components estimated by the counting model. By adopting this configuration, the optimal counting model can be selected based on the difference between the calculated and actual values ​​of the thickness component, thereby achieving more accurate counting. [Effects of the Invention]

[0018] According to the present invention, it is possible to provide a technique for accurately counting objects contained in a laminate.

Brief Description of the Drawings

[0019] [Figure 1] Block diagram of the system of this embodiment. [Figure 2] Hardware configuration diagram of this embodiment. [Figure 3] Overall processing flowchart of the counting device of this embodiment. [Figure 4] Image of signal profile generation of this embodiment. [Figure 5] Processing flowchart of the generation process of this embodiment. [Figure 6] Schematic diagram of the signal profile of this embodiment. [[ID=2,4]] [Figure 7] Processing flowchart of the thickness estimation process of this embodiment. [Figure 8] Schematic diagram of the classification of signal peaks of this embodiment. [Figure 9] Processing flowchart of the model estimation process of this embodiment. [Figure 10] Image of the quadratic surface of this embodiment. [Figure 11] Block diagram of the system of different embodiments.

Modes for Carrying Out the Invention

[0020] Hereinafter, a counting system and a counting method according to an embodiment of the present invention will be described with reference to the drawings. Note that the embodiments shown below are examples of the present invention, and the present invention is not limited to the following embodiments, and various configurations can also be adopted.

[0021] In this embodiment, the configuration and operation of the counting system and counting device will be described, but a counting method, computer program, and program recording medium on which the program is recorded with a similar configuration will produce similar effects. Using a program recording medium, for example, the program can be installed on a computer. The series of processes according to this embodiment, which will be described below, are provided as a program executable by a computer and can be provided via a non-transient computer-readable recording medium such as a CD-ROM or flexible disk, or via a communication line.

[0022] The counting system is comprised of a computer device. The computer device includes an arithmetic unit such as a CPU (Central Processing Unit) and a memory device. The computer device can function as a counting device by executing a counting program stored in the memory device using its arithmetic unit. The counting method is implemented through processing by the computer device, including the counting device.

[0023] In this embodiment, the objects to be counted are plate-shaped or sheet-shaped articles that can be stored in a stacked state. The material of the objects is not particularly limited, but the present invention can achieve accurate counting even for articles that are prone to in-plane deflection, such as steel plates.

[0024] In this embodiment, the laminate includes the objects to be counted. A laminate refers to a structure in which multiple objects are stacked together, forming a single unit. The objects are stacked in one direction to constitute the laminate. The side surface of the laminate (hereinafter referred to as the stacking surface) allows observation of the number of layers of objects, and the quantity of objects can be counted based on the number of layers on the stacking surface. The direction of stacking is not limited and may be vertical, horizontal, or any other direction.

[0025] In this embodiment, objects are counted using image data captured from the laminated surface. Preferably, the image data used for counting is data that captures the entire laminate in at least the stacking direction of the laminated surface. However, the image data used for counting is not limited to data that captures only a portion of the laminate in the stacking direction. Furthermore, the image data used for counting may be data that allows the overall structure of the laminate to be grasped by combining multiple partial data. The thickness of the objects is not particularly limited as long as each object can be distinguished by the pixels of the image data. Therefore, the thickness of countable objects depends on the image data acquisition method (distance, etc.) and the resolution of the imaging device.

[0026] Figure 1 shows a block diagram of the counting system 1. The counting system 1 comprises a counting device 2 and an imaging device 3. The counting device 2 is configured to acquire image data captured by the imaging device 3 via wired or wireless communication. Alternatively, the counting device 2 may incorporate the imaging device 3 and acquire image data independently.

[0027] The counting device 2 includes, as functional components, an image acquisition unit 21 that acquires image data, a generation unit 22 that generates a signal profile based on the image data, a thickness estimation unit 23 that acquires a thickness component estimated based on the signal profile, a model estimation unit 24 that estimates a counting model based on the thickness component, a counting unit 25 that counts objects based on the thickness component, and an output unit 26 that outputs the counting result. The counting device 2 also includes a storage unit DB as a database. The storage unit DB is provided inside or outside the counting device 2 and is not limited to the illustrated example configuration as long as it is capable of various data communication with the counting device 2.

[0028] The control unit 27 is connected to each component and controls each component by issuing control instructions. The control unit 27 is also connected to the storage unit DB and is configured to store and read various types of data. In the following description, the involvement of the control unit 27 will be omitted unless it becomes unclear regarding the operation of each functional component in the counting device 2.

[0029] The counting device 2 is configured to accept user input. The counting device 2 controls the image acquisition unit 21 via the control unit 27 to perform processes such as acquiring image data from the imaging device 3 and selecting image data to be counted, based on the user input.

[0030] The imaging device 3 is a camera or a terminal device equipped with a camera function that can capture image data. The imaging device 3 can transmit the image data to the counting device 2 via wired communication, wireless communication, etc. The image data captured by the imaging device 3 may be acquired by the counting device 2 via an internet communication network, etc. In Figure 1, only one imaging device 3 is shown, but there may be multiple imaging devices 3.

[0031] Figure 2 shows the hardware configuration diagram of the counting device 2. The counting device 2 comprises a control device 201, a storage device 202, a communication device 203, an input device 204, and an output device 205 as its hardware configuration. In this embodiment, the counting device 2 can use computer devices such as a server device or a personal computer. The counting device 2 is composed of multiple computer devices, and as long as the overall functional components (21-27) can be realized, it is not limited to the configuration shown in Figure 2.

[0032] The control device 201 consists of one or more processors such as a CPU and controls the overall processing in the counting device 2 by executing the counting program, OS (Operating System), and other applications. The storage device 202 is an HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, RAM (Random Access Memory), etc., and stores the counting program and various data. The communication device 203 is a communication interface such as wired communication or wireless communication and controls data communication with external devices, including the imaging device 3. The communication device 203 can also perform data communication with external devices by controlling communication with the communication network NW.

[0033] The input device 204 is an input interface that accepts user input and consists of at least one of the following: a touch panel, a mouse, a keyboard, etc. The output device 205 consists of at least one of the following: a display, a speaker, etc., which outputs the processing results from the control device 201.

[0034] Figure 3 shows a flowchart illustrating the overall processing performed by the counting device 2.

[0035] The image acquisition unit 21 acquires image data of the stacked surface of the stacked object captured by the imaging device 3 (S11, image acquisition step). The image acquisition unit 21 may detect the stacked surface included in the image data as a target region and further acquire image data relating to the target region. Here, the image acquisition unit 21 detects the region of the stacked surface used for counting and other regions not used for counting by image recognition, and designates the region of the stacked surface used for counting as the target region. Image recognition can use a machine learning model such as a neural network. There are no limitations on the type of model. The image acquisition unit 21 can input the image data into the machine learning model and acquire image data relating to the target region as output.

[0036] The image acquisition unit 21 may perform image correction processing to make the image data easier to process in subsequent steps. The image correction processing may include trapezoidal correction, and if the stacked surface included in the image data is a trapezoidal region, it may be corrected to a rectangular region.

[0037] The generation unit 22 divides the acquired image data into a plurality of predetermined regions in the stacking direction and the width direction (S12, generation process). The plurality of predetermined regions are set by an arbitrary number of divisions in each of the vertical and horizontal directions (stacking direction and width direction). For example, if the image data is divided into 2 vertically and 1 horizontally, the number of divisions will be 2x1, and the number of divided images after division will be 2. There is no limit to the number of divided images as long as there are multiple divisions; for example, 1x2, 2x2, 4x4, 6x4, etc. can be set. Here, one image divided from the image data is defined as a divided image (predetermined region) to distinguish it from the image data. It is preferable that the number of divisions be multiple (2 or more vertically and 2 or more horizontally) in each of the stacking direction and the width direction.

[0038] The generation unit 22 generates signal profiles for each thickness component of the partitioned image (S12, generation step). The generation step S12 will be described in detail later with reference to Figure 5.

[0039] In this embodiment, the signal profile is obtained by performing a two-dimensional Fourier transform on the grayscale data of the partitioned image, with the horizontal axis representing frequency (1 / z) and the vertical axis representing the intensity of the thickness component (I). The frequency represents the reciprocal of the thickness component (z). The thickness component (z) represents the thickness value per object. The intensity of the thickness component (I) in the signal profile is the intensity that indicates the likelihood that it is that particular thickness component in the partitioned image.

[0040] Figure 4(a) is a reference diagram of image data. In Figure 4(a), the width direction of the stacked material is x, and the stacking direction is y. The position in the width direction and stacking direction in the image data is defined as the image component (x, y).

[0041] Figure 4(b) is a reference diagram of partitioned images obtained by dividing image data into predetermined regions. In Figure 4(b), the number of partitions is 4x4, and the number of partitioned images is 16. Here, each partitioned image is a region demarcated by a dashed line. Note that the dashed lines are added for illustrative purposes only, and do not exist in the actual partitioned images.

[0042] Figure 4(c) shows the grayscale data for one of the partitioned images in Figure 4(b). The grayscale data represents the grayscale of the image at the Y coordinate center of the partitioned image's X coordinate. In Figure 4(c), the vertical axis represents the Y coordinate, and the horizontal axis represents the grayscale value. The grayscale data is obtained as 16 data points corresponding to the 16 partitioned images in Figure 4(b).

[0043] Figure 4(d) is a reference diagram of the signal profile of the thickness component of a partitioned image. In this embodiment, the signal profile is obtained by performing a two-dimensional Fourier transform on the grayscale data. Here, 16 signal profiles are obtained corresponding to the 16 grayscale data in Figure 4(c). The signal profile is converted so that the horizontal axis is frequency (1 / z) and the vertical axis is the intensity (I) of the thickness component. In this way, the thickness component in each predetermined region is obtained based on the signal profile. In an ideal predetermined region without internal deflection or image distortion of the object, one thickness component is obtained as one intensity peak. However, in reality, due to internal deflection, image distortion, etc., multiple thickness components (z) are often obtained as intensity peaks corresponding to each thickness component. Since the multiple thickness components (z) included in the signal profile are indiscriminate and uncertain, it is preferable to narrow them down to one or more appropriate candidates for the true thickness component (z).

[0044] The thickness estimation unit 23 acquires thickness components estimated from each of the predetermined regions (S13, thickness estimation step). The thickness estimation unit 23 classifies the signal peaks extracted based on the thickness components of each predetermined region shown in the signal profile and generates one or more classification results. The thickness estimation unit 23 can output estimated values ​​of multiple thickness components as a classification result of the signal peaks of the signal profile. The thickness estimation step S13 will be described in detail later with reference to Figure 7.

[0045] The model estimation unit 24 calculates model parameters based on input values ​​including the thickness components of each predetermined region, and estimates a counting model having these parameters (S14, model estimation step). In this embodiment, the counting model can output a thickness component (z) for image components (x, y). In this embodiment, the model estimation unit 24 can estimate one or more counting models corresponding to each classification result generated by the thickness estimation unit 23. If there are multiple estimated counting models, the model estimation unit 24 determines the optimal counting model from among the multiple estimated counting models. If there is only one estimated counting model, the model estimation unit 24 may adopt that counting model. The model estimation step S14 will be described in detail later with reference to Figure 9.

[0046] The counting unit 25 counts the objects based on the thickness components of each predetermined region (S15, counting step). The counting unit 25 counts the objects for each predetermined region by detecting the boundary lines of the objects based on the thickness components of each predetermined region. The counting unit 25 can calculate the total number of objects by summing the counting results of the predetermined regions in the stacking direction. In this case, the model estimation step S14 may be omitted, and the objects will be counted based on the thickness components of each predetermined region estimated by the thickness estimation step S13. Also, if the model estimation step S14 is omitted, the model estimation unit 24 becomes unnecessary.

[0047] The counting unit 25 can count objects based on the thickness component estimated by the counting model (S15, counting process). The counting model shows an index of the thickness component that can change in accordance with the coordinates (x,y) in the image data of the object. The counting unit 25 can calculate the total number of objects by referring to the thickness component (z) derived by the counting model for each coordinate (x,y) in the image data of the object and identifying the boundaries of the objects in the thickness direction (stack direction).

[0048] The output unit 26 outputs based on the counting result for the total number of objects by the counting unit 25 (S16, output process). The output unit 26 outputs the counting result as a display on the output device 205 or as sound emitted from a speaker or the like.

[0049] Figure 5 shows a processing flowchart for the generation process in generation step S12.

[0050] The generation unit 22 divides the image data into a plurality of predetermined regions in the stacking direction and the width direction (S21).

[0051] The generation unit 22 performs image processing to binarize each predetermined partitioned region (S22). Here, the image processing is not limited to any processing that prepares the data for the conversion process described later.

[0052] The generation unit 22 converts each predetermined region into a signal profile indicating frequency (S23). In this embodiment, the generation unit 22 converts the image components (x, y) into frequency (1 / z), which is the reciprocal of the thickness component (z), by performing a two-dimensional Fourier transform on the predetermined region. Through the frequency conversion, the signal profile becomes data with frequency (1 / z) on the horizontal axis and the intensity of the thickness component (I) on the vertical axis.

[0053] The generation unit 22 determines the signal peaks of each signal profile (S24). In this embodiment, a predetermined threshold is set for the intensity (I) of the thickness component, and the generation unit 22 determines the intensity (I) of the thickness component that exceeds the threshold as a signal peak. A signal peak represents a predetermined thickness component that has a high intensity that exceeds the threshold. Signal peaks are extracted from the intensity peaks included in the signal profile using the threshold.

[0054] The generation unit 22 determines whether the processing in S22 to S24 has been completed for all predetermined areas (sections). If it has been completed (YES in S25), it completes the processing. If the generation unit 22 has not completed processing for all sections (NO in S25), it returns to S22 and executes the processing for the next section.

[0055] Figure 6(a) illustrates a signal profile in a given region. Here, the horizontal axis of the signal profile represents frequency (1 / z), and the vertical axis represents the intensity of the thickness component (I). Since the thickness component is the reciprocal of the frequency, the thickness component (z) is larger as you move towards the left side of Figure 6(a), and smaller as you move towards the right side.

[0056] Figure 6(b) shows a schematic diagram of the signal profile after determining the signal peak at S24. Examples of thresholds for the intensity of the thickness component are the first threshold I1 and the second threshold I2. For example, if the first threshold I1 is used, signal peak P1 is determined; if the second threshold I2 is used, signal peaks P1 and P2 are determined. These thresholds for the intensity of the thickness component can be arbitrarily set by the user.

[0057] Figure 7 shows a flowchart of the thickness estimation process performed in the thickness estimation step S13.

[0058] The thickness estimation unit 23 classifies the signal peaks included in the signal profile generated by the generation unit 22 (S31). The thickness estimation unit 23 classifies the signal peaks based on the thickness component (z) in each predetermined region included in the signal profile and generates a classification result. The thickness estimation unit 23 classifies signal peaks with similar thickness components (z) across signal profiles as one group. Therefore, the thickness estimation unit 23 classifies the signal peaks based on the thickness component or range of thickness component associated with the classification result across signal profiles. The thickness estimation unit 23 can output the thickness component corresponding to the signal peak. The classification method can employ existing clustering algorithms, such as the k-means method.

[0059] The thickness estimation unit 23 refers to the thickness component associated with each classification result and determines whether it is an invalid value for all signal profiles (S32). For example, in the case of a classification result classified as thickness component 0.2, it is an invalid value if the target signal profile contains an intensity peak corresponding to thickness component 0.2, and an invalid value if it does not contain such an intensity peak.

[0060] If the target signal profile is an invalid value (YES in S32), the thickness estimation unit 23 performs boundary detection processing in a predetermined region corresponding to the signal profile (S33). The boundary detection processing is a detection process by image scanning in the stacking direction of the predetermined region, and determines whether a boundary can be detected in the predetermined region based on the thickness component associated with the classification result. If a boundary can be detected, the number of boundary lines is determined.

[0061] The thickness estimation unit 23 determines in S33 whether the number of boundary lines exceeds a threshold (S34). This threshold is a value for the number of boundary lines and may be adjustable according to the thickness of the object and the image size of a predetermined area.

[0062] If the number of boundary lines exceeds the threshold (YES in S34), the thickness estimation unit 23 determines that the signal peaks included in the signal profile that were deemed invalid are valid (S35). If the number of boundary lines does not exceed the threshold (NO in S34), the thickness estimation unit 23 confirms that the signal peaks included in the signal profile that were deemed invalid are invalid.

[0063] The thickness estimation unit 23 determines whether it has completed the invalid value determination process in S32 for the signal profiles of all predetermined sections classified as identical. If it has completed the process (YES in S36), it proceeds to the process in S37. If the thickness estimation unit 23 has not completed the process for all sections (NO in S36), it returns to S32 and performs the invalid value determination process for the next section.

[0064] The thickness estimation unit 23 determines whether the invalid value determination process in S32 has been completed for the signal profiles of all predetermined sections of all classification results. If it has been completed (YES in S37), it completes the process. If the thickness estimation unit 23 has not completed the process for all classification results (NO in S37), it returns to S32 and performs the invalid value determination process for the other classification results.

[0065] Figure 8 is an overview diagram illustrating the classification of signal peaks included in the signal profile during the thickness estimation process. In Figure 8, the signal profile shows an example with a 3x3 grid of images, but is not limited to this. In Figures 8(c) to (f), shaded signal profiles indicate that signal peaks belonging to the same classification are valid values.

[0066] Figure 8(a) shows the results of determining signal peaks for the signal profiles in S24 of Figure 5. In signal profile S1, signal peaks P1 and P2 were determined, in signal profile S2, signal peak P3 was determined, and in signal profile S3, signal peak P4 was determined.

[0067] Figure 8(b) shows the results of classifying the signal peaks of each signal profile at S31 in Figure 7. In signal profile S1, signal peak P1 is classified as the first signal classification G1, and signal peak P2 is classified as the second signal classification G2. In signal profile S2, signal peak P3 is classified as the second signal classification G2, and in signal profile S3, signal peak P4 is classified as the third signal classification G3. Here, signal peaks P2 and P3 are classified as the same second signal classification G2 because they have similar thickness components (z).

[0068] Figure 8(c) shows the classification results of the first signal classification G1. In the classification results of the first signal classification G1, signal peaks corresponding to the first signal classification G1 included in signal profiles S1 and S5 are considered valid values, while intensity peaks corresponding to the first signal classification G1 included in other signal profiles are considered invalid values.

[0069] Figure 8(d) shows the classification results for the second signal classification G2. In the classification results for the second signal classification G2, signal peaks corresponding to the second signal classification G2 included in signal profiles S1, S2, and S5 are considered valid values, while intensity peaks corresponding to the second signal classification G2 included in other signal profiles are considered invalid values.

[0070] Figure 8(e) shows the classification results for the third signal classification G3. The classification results for the third signal classification G3 are defined as valid values ​​for signal peaks corresponding to the third signal classification G3 included in signal profiles S3, S4, S6, S7, S8, and S9, and invalid values ​​for intensity peaks corresponding to the third signal classification G3 included in other signal profiles.

[0071] Figure 8(f) shows the classification result of the first signal classification G1 after executing the process in Figure 7, which enables invalid values ​​included in the classification result of the first signal classification G1 in Figure 8(c). Here, for signal profile S3, signal peak P5 is newly determined to be an invalid value. The intensity peak corresponding to the first signal classification G1 of signal profile S3 was an invalid value, but as a result of performing boundary detection in S33 assuming it was an invalid signal peak, it was determined to be valid in S34, and updated from an invalid intensity peak to an invalid signal peak in S35. In Figure 8(f), in addition to the signal peaks corresponding to the first signal classification G1 of signal profiles S1 and S5, which were initially valid values, intensity peaks corresponding to the first signal classification G1 other than signal profile S2, which were initially invalid values, are determined to be valid signal peaks P5. Thus, the process in Figure 7 has the effect of detecting the true thickness component by reviving (enabling) the intensity peaks that were overlooked in S24 as valid signal peaks.

[0072] Figure 9 shows a flowchart of the model estimation process in step S14.

[0073] The model estimation unit 24 calculates the parameters of the model and estimates a counting model having those parameters (S41). The counting model is used to derive the thickness component (z) at the coordinates (x, y) of the image component of the image data. In one embodiment, the model estimation unit 24 calculates the parameters of the model using input values ​​that include the thickness component of each predetermined region and estimates a counting model having those parameters. Here, the input values ​​include the reference values ​​of the image component of each predetermined region. The reference values ​​of the image component can be, for example, the center coordinates of each predetermined region, but are not limited to this, and may be any coordinates uniformly set in each predetermined region.

[0074] The counting model can be a function or a pre-trained model. Here, the counting model is not limited by the type of model, as long as it can obtain the thickness component (z).

[0075] In this embodiment, the model employs a function capable of generating a quadratic surface. The function according to this embodiment is given by equation (1). Here, x and y in equation (1) correspond to the coordinates (x, y) in the image before division into a predetermined region, and z in equation (1) corresponds to the thickness component (z) at coordinates (x, y). Note that α, β, γ, δ, ε, and ζ in equation (1) represent coefficients. For example, the image component of the predetermined region is the value of the center position of the predetermined region. The model estimation unit 24 takes all (x, y, z) of signal peaks belonging to the same signal classification as input to derive the parameters of equation (1), and adjusts the coefficients to approximate each input. By adjusting the coefficients, the parameters α', β', γ', δ', ε', and ζ' of the counting model are derived, thereby generating a counting model that is valid for a specific signal classification.

[0076]

number

[0077] In this embodiment, the counting model having the derived parameters represents a quadratic surface of the estimated thickness component (z) that changes in accordance with the image components (x, y).

[0078] The model estimation unit 24 derives a difference value based on the actual value of the thickness component and the calculated value of the thickness component by the counting model (S42). The difference value is used to evaluate the generated counting model. Specifically, the model estimation unit 24 obtains the calculated value of the thickness component from the counting model by inputting reference values ​​(x, y) of the image components of a predetermined region to the estimated counting model. The model estimation unit 24 holds the actual value of the thickness component of the corresponding predetermined region used to generate the counting model. The actual value of the thickness component may be the thickness component corresponding to the signal peak. The model estimation unit 24 calculates the difference between the actual value of the thickness component and the calculated value of the thickness component. The model estimation unit 24 calculates the difference similarly for all predetermined regions and derives a difference value as the sum of these differences. The difference value can serve as an accuracy index of the counting model.

[0079] The model estimation unit 24 performs the processes S41 and S42 on all classification results (S43).

[0080] The model estimation unit 24 determines the optimal counting model based on the difference value corresponding to each classification result (S44). For example, if there are three classification results, three counting models are estimated, and their respective difference values ​​are derived in S42. The model estimation unit 24 can then select the counting model with the smallest difference value as having the smallest difference from the actual value and good accuracy.

[0081] In this way, by adopting the most suitable counting model from among several counting models estimated from the classification results of multiple thickness components, the accuracy of the counting can be improved.

[0082] The counting unit 25 performs object counting processing while referring to the counting model. Figure 10 is an illustrative diagram of the counting model showing a quadratic surface. In Figure 10, the thickness component (z) corresponding to the image component (x, y) is shown as a quadratic surface. The counting unit 25 uses the quadratic surface to perform object counting while referring to the estimated value (z) of the thickness component for the image component (x, y) in the image data. In this way, by referring to the counting model, it is possible to accurately count objects in response to thickness (z) that changes depending on the width direction (x) and the stacking direction (y).

[0083] In this embodiment, the counting unit 25 counts objects in the image data by referring to a quadratic surface. Specifically, the counting unit 25 refers to a quadratic surface when detecting the boundary lines of objects in the image data. As an example, first, the counting unit 25 calculates the thickness component (z) at a reference position near the center of the image data by substituting the coordinates (x, y) of the image component at that reference position into the quadratic surface. Next, the counting unit 25 searches the image data in the stacking direction using the calculated thickness component as a clue to detect the boundary line of an object having a laminate thickness that matches the calculated thickness component (z). Subsequently, the counting unit 25 calculates the thickness component (z) at the location by substituting the coordinates (x, y) near the detected boundary line into the quadratic surface. Here, the coordinates (x, y) near the boundary line may be the position obtained by adding z / 2 (half the thickness component) from the boundary line in the stacking direction. The counting unit 25 searches for the next boundary line at a position obtained by adding only the thickness component (z) calculated from the detected boundary line in the stacking direction. By repeating these processes up to the top and bottom ends in the stacking direction, all objects contained in the stack can be counted accurately using the thickness component which changes according to the image components.

[0084] <Embodiment 2> Figure 11 shows a block diagram of the counting system 10 in Embodiment 2. According to Figure 11, the counting system 10 comprises a counting device 11 and a terminal device 12, which are configured to communicate with each other via a communication network NW. Here, there may be multiple terminal devices 12.

[0085] The terminal device 12 can be a smartphone, tablet, personal computer, or the like. Preferably, the terminal device 12 is configured to include an imaging device. In Embodiment 2, the terminal device 12 transmits the image data to be counted to the counting device 11.

[0086] The counting device 11 can use a computer such as a server device. The counting device 11 is composed of one device or a combination of multiple devices. The counting device 11 can take the form of, for example, a cloud server.

[0087] The counting device 11 has the same functional components (21-26) as the counting device 2. Some of the functional components may be implemented by the terminal device 12. The counting device 11 receives image data from the terminal device 12 and counts the objects in the image data. The counting device 11 transmits the counting result to the terminal device 12.

[0088] The terminal device 12 can receive the counting result from the counting device 11 and display the result on a display or other output device.

[0089] The counting system 10 of Embodiment 2 can, for example, take the form of a web service, which can suitably assist users in obtaining counting results of objects using a terminal device 12. [Explanation of symbols]

[0090] 1. Counting System 2. Counting device 21 Image acquisition unit 22 Generation part 23 Thickness estimation section 24 Model Estimation Unit 25 Counting Section 26 Output section 27 Control Unit DB storage 3. Imaging device 10 Counting Systems 11 Counting device 12 Terminal devices NW (Network Communication Network)

Claims

1. A method for counting objects contained in a laminate, The computer performs the image acquisition process, the generation process, the thickness estimation process, and the counting process. The aforementioned image acquisition step acquires image data of the stacked surface of the stacked material, The generation step divides the image data into a plurality of predetermined regions in the stacking direction and the width direction, and generates a signal profile showing the thickness component of each predetermined region. The thickness estimation step obtains the thickness components estimated from each of the predetermined regions, The counting step is a counting method for counting the objects based on the thickness component.

2. The thickness estimation step generates a classification result of signal peaks extracted based on the thickness component included in the signal profile, The counting method according to claim 1, wherein the counting step counts the objects based on the thickness component corresponding to each of the signal peaks of the classification result.

3. The thickness estimation step determines, according to the intensity of thickness components exceeding a predetermined level, the signal peaks that are valid values ​​or the intensity peaks that are invalid values ​​included in the signal profile in the classification result. If a predetermined number or more boundary lines of the predetermined region are detected based on the thickness component in the classification result, the invalid value is determined to be the valid value. The counting method according to claim 2, wherein the counting step counts the object based on the thickness component indicated by the signal profile determined to be the valid value.

4. The system includes a model estimation step that calculates model parameters using input values ​​that include the thickness components of each predetermined region, and estimates a counting model having those parameters. The counting method according to any one of claims 1 to 3, wherein the counting step counts the object based on the thickness component estimated by the counting model.

5. The model parameters are calculated using input values ​​that include the thickness components of each predetermined region, and multiple counting models having these parameters, corresponding to each classification result, are estimated. The system includes a model estimation step that determines the optimal counting model from a plurality of counting models corresponding to the classification result, based on the difference between the actual values ​​of the thickness components of each predetermined region and the calculated values ​​of the thickness components by the counting model. The counting method according to claim 2 or 3, wherein the counting step counts the objects based on the thickness component estimated by the counting model.

6. A counting system for objects contained in a laminate, It comprises an image acquisition unit, an image generation unit, a thickness estimation unit, and a counting unit. The image acquisition unit acquires image data of the stacked cells of the stacked material, The generation unit divides the image data into a plurality of predetermined regions in the stacking direction and the width direction, and generates a signal profile showing the thickness component of each predetermined region. The thickness estimation unit obtains the thickness components estimated from each of the predetermined regions, The counting unit is a counting system that counts the objects based on the thickness component.

7. A program for counting objects contained in a laminate, The computer functions as an image acquisition unit, an image generation unit, a thickness estimation unit, and a counting unit. The image acquisition unit acquires image data of the stacked cells of the stacked material, The generation unit divides the image data into a plurality of predetermined regions in the stacking direction and the width direction, and generates a signal profile showing the thickness component of each predetermined region. The thickness estimation unit obtains the thickness components estimated from each of the predetermined regions, The counting unit is a counting program that counts the objects based on the thickness component.

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