Inspection device, parameter setting method, and parameter setting program

Abstract

To provide an inspection device, a parameter setting method, and a parameter setting program, which can automatically set setting information by inputting necessary conditions when setting the setting information corresponding to a new product.SOLUTION: An inspection device 1 includes: an attribute information acquisition section 101 that acquires attribute information on a translucent container to be inspected and liquid in the container; a parameter set acquisition section 102 that acquires a parameter set corresponding to the attribute information acquired by the attribute information acquisition section 101 from among a plurality of parameter sets which are setting information prepared in advance according to attributes of the container and the liquid; and a parameter setting section 103 that sets the parameter set acquired by the parameter set acquisition section as setting information of the inspection device.SELECTED DRAWING: Figure 8

Description

[Technical Field]

[0001] The present disclosure relates to setting configuration information for an inspection device that inspects a liquid contained in a container. [Background technology]

[0002] Conventionally, inspection devices have been proposed for detecting foreign matter in a liquid contained in a container. As an example of such an inspection device, Patent Document 1 discloses an inspection device that executes an imaging process for imaging a container made of a light-transmitting material that contains a liquid, an image processing process for filtering the image of the photographed container, and a detection process for detecting foreign matter contained in the container based on the filtered image. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-122072 [Patent Document 2] Japanese Patent Application Laid-Open No. 2002-148212 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when inspecting a new product using the inspection device disclosed in Patent Document 1, there is a problem in that an operator must manually enter various setting information corresponding to the new product.

[0005] In this regard, as an example of a technology for automatically setting setting information, the X-ray inspection device disclosed in Patent Document 2 transports an object to be inspected in advance using a transport mechanism before normal operation, and automatically sets image processing parameters such as image correction coefficients and image correction constants for determining the intensity of X-rays or the presence of foreign matter. However, the X-ray inspection device disclosed in Patent Document 2 does not automatically set setting information by inputting necessary conditions when setting information corresponding to a new product, and therefore cannot solve the above-mentioned problem.

[0006] In view of the above-mentioned problems, one of the objects of the present disclosure is to provide an inspection device, a parameter setting method, and a parameter setting program that can automatically set setting information corresponding to a new product by inputting necessary conditions. [Means for solving the problem]

[0007] An inspection apparatus according to an exemplary embodiment includes: an attribute information acquisition unit that acquires attribute information of a translucent container that is an inspection target and a liquid in the container; a parameter set acquisition unit that acquires a parameter set corresponding to the attribute information acquired by the attribute information acquisition unit from a plurality of parameter sets that are setting information prepared in advance according to the attributes of the container and the liquid; The apparatus further includes a parameter setting unit that sets the parameter set acquired by the parameter set acquisition unit as setting information for the inspection device.

[0008] A parameter setting method according to an exemplary embodiment includes: Acquire attribute information of the translucent container to be inspected and the liquid in the container; acquiring a parameter set corresponding to the acquired attribute information from a plurality of parameter sets which are setting information prepared in advance according to the attributes of the container and the liquid; The acquired parameter set is set as the setting information of the inspection device.

[0009] A parameter setting program according to an exemplary embodiment includes: For computers, acquiring attribute information of the translucent container to be inspected and the liquid therein; acquiring a parameter set corresponding to the acquired attribute information from a plurality of parameter sets which are setting information prepared in advance according to the attributes of the container and the liquid; The acquired parameter set is set as the setting information of the inspection device. [Effects of the Invention]

[0010] The present disclosure makes it possible to provide an inspection device, a parameter setting method, and a parameter setting program that can automatically set setting information by inputting the necessary conditions when setting information corresponding to a new product. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating a configuration of an inspection apparatus according to an exemplary embodiment. [Figure 2] FIG. 2 is a front view showing a container to be inspected that is placed in the inspection device. [Figure 3] FIG. 10 is a diagram illustrating an example of parameters related to erroneous detection of a foreign substance. [Figure 4] 10 is a flowchart illustrating an example of a process executed by an inspection apparatus according to an exemplary embodiment. [Figure 5] 10 is a flowchart illustrating another example of a process performed by the inspection apparatus according to an exemplary embodiment. [Figure 6] 10 is a flowchart illustrating another example of a process performed by the inspection apparatus according to an exemplary embodiment. [Figure 7] 10 is a flowchart illustrating an example of a parameter correction process executed by an inspection apparatus according to an exemplary embodiment. [Figure 8] FIG. 1 is a diagram illustrating major components of an inspection apparatus according to an exemplary embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] An exemplary embodiment will be described below with reference to the drawings. FIG. 1 is a diagram showing the configuration of an inspection device 1 according to an exemplary embodiment. The inspection device 1 is a device for detecting foreign matter in a container made of a light-transmitting material. The foreign matter in the container is expected to be pieces of cork, plastic, metal, etc. The inspection device 1 includes a processor 10, an input device 11, a rotation mechanism 12, an illumination device 13, an imaging device 14, a communication interface (I / F) 15, and a storage device 16.

[0013] The processor 10 is a computing device that performs overall control of the inspection device 1. Specific examples of the processor 10 include processors such as a CPU (Central Processing Unit) and an MPU (Micro Processing Unit). The processor 10 executes a parameter setting method according to an exemplary embodiment by reading and executing a parameter setting program 100 from the storage device 16. The program executed by the processor 10 may be executed by an integrated circuit such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The processor, FPGA, ASIC, and other integrated circuits correspond to computers.

[0014] The input device 11 is a device that allows an operator to input or select attribute information of the container to be inspected and the liquid in the container. A specific example of the input device 11 is a touch panel. The attributes of the container and the liquid include the type of container shape, the container size, the container color, and the liquid color. For example, if the liquid is wine, the types of container shape include Bordeaux type, Burgundy type, sparkling wine type (champagne type), Rhein / Mosel type, Bocksbeutel type, etc. The types of container size include, for example, full bottle size and half bottle size. The color of the container is, for example, green or transparent. The color of the liquid is, for example, red, white, or rosé. Note that liquids other than wine can also be inspected.

[0015] The parameters according to the attributes of the container and liquid being inspected can be broadly divided into (1) parameters according to the type of shape and size of the container being inspected, and (2) parameters according to the color of the container and the color of the liquid being inspected.

[0016] Parameters according to the type of shape and size of the container to be inspected include, for example, identification information for the operating program, parameters relating to the position of the clamp that holds the container to be inspected, parameters relating to the timing of imaging, parameters specifying the image processing area of ​​the container to be inspected, parameters specifying the offset of the measurement position in the vertical axis direction of the container, parameters relating to measuring the edge of the container, parameters relating to determining the width of the container, parameters relating to the process of masking the outline of the container, image processing areas relating to determining the type of liquid, parameters relating to measuring the liquid level, parameters indicating the start point and end point in the vertical axis direction of the captured image, etc. Values ​​are set for each of these parameters according to the type of shape of the container.

[0017] The parameters according to the color of the container and the color of the liquid to be inspected include the light intensity and light emission width of the light source, the gain and exposure time of the imaging device, parameters related to determining the presence or absence of the container to be inspected, parameters related to alignment processing, upper and lower limit values ​​of the binarization level related to the type of liquid discrimination, parameters related to median processing, parameters related to the difference image, etc. Values ​​for these parameters are set according to the color of the container and the color of the liquid, respectively.

[0018] The rotation mechanism 12 is a device that rotates the container to be inspected around a vertical rotation axis and a horizontal rotation axis as shown in FIG. 2. In this embodiment, the vertical rotation axis is a vertical rotation axis located between the two containers 20, 21 to be inspected. The horizontal rotation axis is a rotation axis perpendicular to the vertical rotation axis and extends near the drinking spouts of the containers 20, 21 to be inspected. In the initial state, the containers 20, 21 to be inspected are positioned so that their longitudinal axes are parallel to the vertical rotation axis. The rotation mechanism 12 can rotate the container to be inspected around the horizontal rotation axis by a plurality of predetermined rotation angles. Furthermore, the rotation mechanism 12 can rotate the container to be inspected around the vertical rotation axis by a predetermined rotation angle. The predetermined rotation angle can be, for example, 0 to 180°.

[0019] The lighting device 13 is a device for illuminating the container and liquid to be inspected. The photographing device 14 is a device for photographing the container and liquid to be inspected and generating a photographed image. The inspection device 1 is equipped with two photographing devices 14. The lighting device 13 and the photographing device 14 are installed in opposing positions across the container to be inspected. When the container to be inspected is in an upright position, the photographing device 14 is located on the front side of the inspection device 1, and the lighting device 13 is located on the back side. The lighting device 13 and the photographing device 14 are rotated together with the container to be inspected by the rotation mechanism 12. Therefore, the rotation action by the rotation mechanism 12 does not change the positional relationship between the container to be inspected, the lighting device 13, and the photographing device 14. Therefore, the photographing device 14 can photograph the container in an upright position and an inclined position for each of the two containers to be inspected. Note that the number of containers to be inspected is not limited to two, and may be one or three or more.

[0020] The photographing device 14 photographs the container to be detected and generates a photographed image when a predetermined time (e.g., 100 milliseconds, 300 milliseconds, 500 milliseconds, etc.) has elapsed after the container to be detected has been rotated by the rotation mechanism 12 around the horizontal rotation axis and / or the vertical rotation axis at a predetermined rotation angle and has come to rest. As a result, the photographed image that is generated will show air bubbles and foreign matter moving within the liquid in the container.

[0021] The communication interface 15 communicates data between the inspection device 1 and other devices (not shown). The storage device 16 is a storage device that stores various information such as the parameter setting program 100 executed by the processor 10 and parameter sets.

[0022] The parameter setting program 100 includes an attribute information acquisition unit 101, a parameter set acquisition unit 102, a parameter setting unit 103, an inspection process control unit 104, an image processing unit 105, a foreign object detection unit 106, a parameter correction unit 107, and an erroneous detection determination unit 108.

[0023] The attribute information acquisition unit 101 is a program that acquires attribute information of the translucent container to be inspected and the liquid in the container. The attribute information acquisition unit 101 can acquire attribute information of the container and the liquid in the container that is input by the operator using the input device 11.

[0024] The parameter set acquisition unit 102 is a program that acquires a parameter set corresponding to the attribute information acquired by the attribute information acquisition unit 101 from a plurality of parameter sets, which are setting information prepared in advance according to the attributes of containers and liquids. In this embodiment, parameter sets corresponding to the number of combinations of container shape types, container sizes, container colors, and liquid colors are prepared and stored in the storage device 16. For example, if there are five container shape types, three container sizes, two container colors, and five liquid colors, 150 parameter sets are prepared. The parameter set acquisition unit 102 acquires, from the storage device 16, parameter sets corresponding to the attribute information acquired by the attribute information acquisition unit 101. Identification information corresponding to the container shape type, container size, container color, and liquid color is assigned to each parameter set. For example, a parameter set corresponding to a Bordeaux-style container shape, a full-bottle size container size, a green container color, and a red liquid color is assigned a first identification information, and a parameter set corresponding to a Burgundy-style container shape, a full-bottle size container size, a green container color, and a red liquid color is assigned a second identification information.

[0025] Such a parameter set may be registered in a storage device of an external device with which the inspection device 1 can communicate data, instead of in the storage device 16 of the inspection device 1. In this case, the parameter set acquisition unit 102 acquires the parameter set identified by the identification information of the parameter set from the storage device of the external device via the communication interface 15.

[0026] The parameter setting unit 103 is a program that sets the parameter set acquired by the parameter set acquisition unit 102 as setting information of the inspection device 1. The parameter setting unit 103 sets the acquired parameter set as setting information of the inspection device 1 by saving the parameter set in a setting information saving area of ​​the storage device 16.

[0027] The inspection process control unit 104 is a program that controls the inspection process for detecting foreign matter contained in a liquid contained in a container to be inspected, based on the parameter set set by the parameter setting unit 103. In the inspection process, the container to be inspected is rotated around a horizontal rotation axis and a vertical rotation axis, and an image of the container is taken. The inspection process control unit 104 rotates the container to be inspected via the rotation mechanism 12 by executing an operating program that performs a rotation operation according to the type of shape and size of the container to be inspected. The inspection process control unit 104 also controls the imaging device 14 to capture an image of the container to be inspected in a stationary state, and generate a captured image of the container.

[0028] The image processing unit 105 is a program that executes image processing on a captured image of a container to be inspected. Specifically, the image processing unit 105 can execute alignment processing, container presence / absence determination processing, container width measurement processing, liquid type determination processing, contour mask generation processing, and label processing on the captured image of the container to be inspected, using parameters set by the parameter setting unit 103.

[0029] In the alignment process, the image processing unit 105 performs a matching process based on the outline of a predetermined container on the captured image to identify the position of the container in the captured image. In the container presence determination process, the image processing unit 105 determines whether a container is present in the captured image based on the average and variance of the brightness of the captured image. In the container width measurement process, the image processing unit 105 detects the edge of the container in the width direction and measures the width of the container. In the liquid type determination process, the image processing unit 105 determines whether the type of liquid in the captured image is the type of liquid specified by the setting information. In the contour mask generation process, the image processing unit 105 generates a contour mask corresponding to the outline of the container in the captured image. In the label processing, the image processing unit 105 identifies the position of the label affixed to the container in the captured image and identifies the position of the gap in the label.

[0030] The image processing unit 105 also generates a difference image from multiple captured images generated by continuously capturing images of the container to be inspected. For example, the image processing unit 105 generates a difference image between an image captured 100 milliseconds after the container to be inspected is rotated and stopped, and an image captured 300 milliseconds after that. The image processing unit 105 also generates a difference image between the difference image generated in this manner and an image captured 500 milliseconds after the container to be inspected is rotated and stopped. If the container to be inspected contains a foreign object, the difference image may display the trajectory of the foreign object or air bubbles moving in the liquid. If the container to be inspected does not contain a foreign object, the difference image may display the trajectory of air bubbles moving in the liquid.

[0031] Furthermore, the image processing unit 105 divides the differential image thus generated into multiple image regions, and then performs median filter processing, binarization processing, mask processing, and opening processing on each divided image region using the parameters set by the parameter setting unit 103.

[0032] Furthermore, the image processing unit 105 uses the parameters corrected by the parameter correction unit 107 described later and the parameters set by the parameter setting unit 103 to perform median filter processing, binarization processing, mask processing, and opening processing on each divided image area.

[0033] The foreign object detection unit 106 is a program that executes a foreign object detection process to detect foreign objects in the liquid in the container using a difference image that has been subjected to median filtering, binarization, masking, and opening processes by the image processing unit 105. More specifically, the foreign object detection unit 106 detects pixel regions in the difference image that have a brightness value different from that of the background image region and are smaller than a predetermined area (hereinafter referred to as the "lower area limit"). The foreign object detection unit 106 then determines pixel regions in the difference image that are equal to or larger than the lower area limit as foreign objects. The lower area limit is preferably set to the area in pixel units of the smallest foreign object that may exist in the container. In other words, the lower area limit is preferably set to an area slightly larger than the maximum area of ​​an air bubble that may exist in the container. The initial value of this lower area limit is set by the parameter setting unit 103. The lower area limit is corrected by the parameter correction unit 107, which will be described later.

[0034] The parameter correction unit 107 is a program that corrects parameters related to the erroneous detection of a foreign object when the foreign object detection unit 106 erroneously detects a foreign object. In other words, the parameter correction unit 107 corrects parameters related to the erroneous detection of a foreign object when the foreign object detection unit 106 detects an air bubble in a liquid as a foreign object. The parameters related to the erroneous detection of a foreign object include (1) a lower limit value of area used in the foreign object detection process executed by the foreign object detection unit 106 and (2) parameters related to the image processing executed by the image processing unit 105. The image processing includes median filtering, binarization, masking, and opening. FIG. 3 shows an example of parameters related to the erroneous detection of a foreign object. The parameters related to the erroneous detection of a foreign object can be set for each divided image region and for each predetermined operation executed by the operating program.

[0035] The area lower limit used in the foreign object detection process is a threshold that defines a predetermined area equivalent to the minimum area of ​​a foreign object in the difference image. In the foreign object detection process, pixel areas in the binarized difference image that have brightness values ​​different from the background image area and are equal to or greater than the area lower limit are detected as foreign objects. Therefore, by increasing the area lower limit, tiny air bubbles in the liquid are less likely to be detected as foreign objects, thereby reducing false detection of foreign objects.

[0036] The parameters relating to the image processing executed by the image processing unit 105 include parameters relating to median filtering, binarization, masking, and opening.

[0037] A specific example of a parameter related to median filtering is a parameter that specifies the pixel-unit range over which median filtering is performed. For example, if the median is "7," then a 7-pixel by 7-pixel area corresponds to the unit pixel area over which median filtering is performed. The parameter that specifies the median filter area corresponds to the "median" parameter in Figure 3. Increasing the value of this parameter, i.e., expanding the area over which median filtering is performed, reduces false detection of foreign objects.

[0038] A specific example of a parameter related to the binarization process is the threshold value used in the binarization process. The difference image is binarized based on this threshold value. For example, if the difference image is expressed in 256 gradations, this threshold value is a value greater than 0 (black) and less than 255 (white). For example, if the background image region of the difference image is black (brightness: 0), in other words, if pixel regions with relatively high brightness values ​​are expressed as foreign objects, pixel regions with brightness values ​​greater than this threshold value may be detected as foreign objects. Therefore, by increasing this threshold value, the number of pixel regions that may be detected as foreign objects decreases, thereby suppressing erroneous detection of foreign objects.

[0039] Furthermore, if the background image region of the difference image is white (brightness: 255), in other words, if pixel regions with relatively low brightness values ​​are represented as foreign objects, pixel regions with brightness values ​​smaller than this threshold value may be detected as foreign objects. Therefore, by reducing this threshold value, the number of pixel regions that may be detected as foreign objects is reduced, thereby suppressing false detection of foreign objects. The threshold value used in the binarization process may be set for each divided image region of the difference image.

[0040] A specific example of a parameter related to masking is the standard deviation of bubble size. In masking, pixel regions that make up the differential image and have a size that falls within the range of the standard deviation of the bubble size are masked. The standard deviation of bubble size is the standard deviation of the size of the holes in multiple bubbles that may be contained in the liquid being inspected. This parameter corresponds to the "hole standard deviation" parameter in Figure 3. Increasing the value of this parameter, i.e., increasing the variation in the size of pixel regions detected as bubbles, will result in more pixel regions being detected as bubbles and masked, thereby reducing false detection of foreign objects.

[0041] A specific example of a parameter related to the opening process is a parameter that specifies the pixel-by-pixel range from which noise is removed by the opening process. This parameter corresponds to the "noise removal" parameter in Figure 3. Increasing the value of this parameter, i.e., expanding the range from which noise is removed, reduces false detection of foreign objects.

[0042] The average brightness of bubbles is a parameter that specifies the average brightness of bubbles that may be contained in the liquid being inspected. This parameter is used in the masking process and opening process. This parameter corresponds to the "average brightness of holes" parameter in Figure 3. Increasing the value of this parameter makes it easier to distinguish between foreign objects and bubbles, thereby reducing false detection of foreign objects.

[0043] The parameter correction unit 107 increases or decreases the value of a parameter related to the above-mentioned erroneous detection of a foreign object depending on the type of the parameter. For example, if the parameter's value is such that erroneous detection of a foreign object is suppressed by increasing the parameter's value, the parameter correction unit 107 increases the parameter's value by a preset value. The amount by which the parameter's value is increased can be determined depending on the degree of influence of the parameter on erroneous detection of a foreign object. For example, the amount by which the parameter's value is increased can be specified by an operator. Alternatively, a predetermined amount determined depending on the degree of influence of the parameter on erroneous detection of a foreign object may be used as the amount by which the parameter's value is increased.

[0044] On the other hand, if the parameter value is such that reducing the parameter value will reduce false detection of a foreign object, the parameter correction unit 107 reduces the parameter value by a preset value. The amount of reduction in the parameter value can be determined depending on the degree of influence of the parameter value on false detection of a foreign object.

[0045] The erroneous detection determination unit 108 is a program that determines whether or not a foreign object has been erroneously detected based on the result of the foreign object detection process executed by the foreign object detection unit 106.

[0046] FIG. 4 is a flowchart showing an example of processing executed by the inspection device 1 according to an exemplary embodiment.

[0047] In step S1, the attribute information acquisition unit 101 of the inspection device 1 acquires attribute information of the container to be inspected and the liquid in the container. In step S2, the parameter set acquisition unit 102 acquires a parameter set corresponding to the attribute information acquired by the attribute information acquisition unit 101 from a plurality of parameter sets prepared in advance. In step S3, the parameter setting unit 103 sets the parameter set acquired by the parameter set acquisition unit 102 as the initial value of the setting information of the inspection device 1.

[0048] In step S4, the inspection process control unit 104 executes an inspection process on the liquid in the container that does not contain any foreign matter, based on the parameter set set by the parameter setting unit 103.

[0049] In step S5, the image processing unit 105 acquires a plurality of captured images obtained by the inspection process. In step S6, the image processing unit 105 executes image processing on the acquired captured images. Details of the image processing executed by the image processing unit 105 will be described in detail with reference to FIG. 5. Note that this image processing executes a foreign matter inspection process that determines whether or not a foreign matter is present in the container to be inspected.

[0050] In step S7, parameter correction unit 107 determines whether or not a foreign object has been detected based on the foreign object detection result obtained in the processing of step S5. If it is determined that a foreign object has not been detected (NO), the processing of Fig. 4 ends. On the other hand, if it is determined that a foreign object has been detected (YES), the processing branches to step S8.

[0051] In step S8, the parameter correction unit 107 executes a parameter correction process to correct parameters related to the erroneous detection of foreign matter based on the foreign matter detection results generated by the foreign matter inspection process, and the process of FIG. 4 ends.

[0052] FIG. 5 is a flowchart showing an example of image processing executed by the inspection device 1 according to an exemplary embodiment.

[0053] In step S10 shown in FIG. 5, the image processing unit 105 generates a difference image based on a plurality of captured images. In this embodiment, the image processing unit 105 generates a grayscale difference image. More specifically, in the first step S10, the image processing unit 105 generates a difference image between the first captured image and the second captured image. In the second step S10, the image processing unit 105 generates a difference image between the third captured image and the difference image generated in the first step S10. In this way, the image processing unit 105 generates difference images for all of the generated captured images.

[0054] In step S11, the image processing unit 105 divides the difference image into a plurality of image regions. Each divided region corresponds to a divided image region. In step S12, the process shown in FIG. 6 is executed for each divided image region of the generated difference image.

[0055] In step S13, the image processing unit 105 determines whether the processes of steps S10 to S12 have been executed for all of the acquired photographed images. If it is determined that the processes of steps S10 to S12 have not been executed for all of the acquired photographed images (NO), the process returns to step S10, and step S10 is executed a second time. On the other hand, if it is determined that the processes of steps S10 to S12 have been executed for all of the acquired photographed images (YES), the process of FIG. 5 ends.

[0056] 6 is a flowchart showing another example of the process executed by the inspection device 1 according to an exemplary embodiment. This process corresponds to step S12 in FIG.

[0057] In step S20, the image processing unit 105 selects one divided image area from the plurality of divided image areas and performs median filtering on the selected divided image area. In step S21, the image processing unit 105 performs binarization processing on the divided image area on which the median filtering has been performed.

[0058] In step S22, the image processing unit 105 performs masking on the binarized divided image region. In this masking, bubbles in the divided image region are detected based on pattern matching that detects shapes identical to bubbles, and the bubbles are masked. Note that depending on the set parameter values, there is a possibility that not all bubbles will be masked.

[0059] In step S23, the image processing unit 105 performs opening processing on the divided image region that has been subjected to mask processing, thereby removing white noise within the divided image region.

[0060] In step S24, the foreign substance detection unit 106 performs foreign substance detection processing on the divided image region on which the opening processing has been performed.

[0061] In step S25, image processing unit 105 determines whether foreign substance detection processing has been performed on the last segmentation target region in the immediately preceding step S24. If it is determined that foreign substance detection processing has not been performed on the last segmentation target region (NO), the process returns to step S20. In this case, the processes of steps S20 to S24 are performed on divided image regions that have not yet been selected. On the other hand, if it is determined that foreign substance detection processing has been performed on the last segmentation target region (YES), the process of FIG. 6 ends.

[0062] 7 is a flowchart showing an example of a parameter correction process executed by the inspection apparatus 1 according to an exemplary embodiment. In step S30, the parameter correction unit 107 corrects the value of a parameter related to the erroneous detection of a foreign substance according to the type of the parameter.

[0063] In step S31, the image processing unit 105 performs the image processing shown in FIG. 6 on the captured image already acquired in step S5 of FIG. 4, using the parameters related to the erroneous detection of foreign matter corrected in step S30.

[0064] In step S32, the erroneous detection determination unit 108 determines whether or not there has been an erroneous detection of a foreign object based on the results of the foreign object detection process executed using the parameters related to the erroneous detection of a foreign object corrected in step S30. If it is determined that there has been an erroneous detection of a foreign object (YES), the process returns to step S30. On the other hand, if it is determined that there has not been an erroneous detection of a foreign object (NO), the parameter correction process of FIG. 7 ends.

[0065] FIG. 8 is a diagram showing the main components of an inspection device 1 according to an exemplary embodiment. The inspection device 1 includes an attribute information acquisition unit 101, a parameter set acquisition unit 102, and a parameter setting unit 103. The attribute information acquisition unit 101 acquires attribute information of a translucent container to be inspected and the liquid in the container. The parameter set acquisition unit 102 acquires a parameter set corresponding to the attribute information acquired by the attribute information acquisition unit from among a plurality of parameter sets, which are setting information prepared in advance according to the attributes of the container and the liquid. The parameter setting unit 103 sets the parameter set acquired by the parameter set acquisition unit 102 as setting information for the inspection device.

[0066] By adopting this configuration, the setting information can be automatically set by inputting the attribute information of the container and the liquid in the container, which is a necessary condition when setting the setting information for a new product. Therefore, compared to setting the setting information manually, the setting time can be significantly reduced and the burden on the worker can be reduced.

[0067] The attributes of the container and the liquid include the type of container shape, the color of the container, and the color of the liquid. Therefore, the inspection device 1 can automatically set a parameter set corresponding to the type of container shape, the color of the container, and the color of the liquid of a new product from among a plurality of parameter sets prepared in advance according to the type of container shape, the color of the container, and the color of the liquid.

[0068] Furthermore, in the above-described embodiment, the foreign object detection unit 106 performs foreign object detection processing to detect foreign objects in the liquid inside the container by determining whether a pixel area of ​​a predetermined area or more exists in the image obtained by performing image processing on a captured image of the container when the container does not contain any foreign objects. If the foreign object detection unit 106 erroneously detects a foreign object, the parameter correction unit 107 corrects parameters related to the erroneous detection of a foreign object. This makes it possible to automatically correct parameters related to the erroneous detection of a foreign object even when a foreign object is erroneously detected, thereby improving the accuracy of foreign object detection.

[0069] Furthermore, in the above-described embodiment, the image processing unit 105 performs image processing using the parameters related to the erroneous detection of a foreign object corrected by the parameter correction unit 107. Next, the foreign object detection unit 106 performs foreign object detection using the parameters related to the erroneous detection of a foreign object corrected by the parameter correction unit 107. The parameter correction unit 107 corrects the parameters related to the erroneous detection of a foreign object until the erroneous detection of a foreign object by the foreign object detection unit 106 is eliminated. This corrects the parameters related to the erroneous detection of a foreign object to values ​​that do not cause erroneous detection of a foreign object, thereby improving the accuracy of foreign object detection.

[0070] In another embodiment, the inspection device 1 may include a code information acquisition unit that acquires code information such as a one-dimensional code or a two-dimensional code attached to a container to be inspected. Specific examples of the code information acquisition unit include a barcode reader and a two-dimensional code reader. In this embodiment, the code information attached to the container is associated with identification information of the parameter set, and the data table may be registered in the storage device 16 of the inspection device 1 or in the storage device of an external device with which the inspection device 1 can communicate data.

[0071] The attribute information acquisition unit 101 refers to the data table and identifies the identification information of the parameter set associated with the code information acquired by the code information acquisition unit. Then, the attribute information acquisition unit 101 acquires, from a plurality of parameter sets prepared in advance, a parameter set identified by the identification information of the parameter set associated with the code information acquired by the code information acquisition unit.

[0072] By employing this configuration, an operator can input code information of a product to be inspected, and automatically set the setting information corresponding to that product.

[0073] In the above examples, the program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored on a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable medium or tangible storage medium includes random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disk (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable medium or communication medium includes electrical, optical, acoustic, or other forms of propagated signals.

[0074] The present disclosure is not limited to the above-described embodiments, and can be modified as appropriate within the scope of the present disclosure. [Explanation of symbols]

[0075] 1. Inspection equipment 4. Divided image area 10 processors 11 Input Devices 12 Rotation mechanism 13 Lighting equipment 14 Imaging equipment 15 Communication Interface 16 Storage device 100 Parameter Setting Program 101 Attribute information acquisition section 102 Parameter set acquisition unit 103 Parameter setting section 104 Inspection processing control unit 105 Image processing section 106 Foreign object detection unit 107 Parameter Correction Unit 108 False detection judgment unit

Claims

1. an attribute information acquisition unit that acquires attribute information of a translucent container that is an inspection target and a liquid in the container; a parameter set acquisition unit that acquires a parameter set corresponding to the attribute information acquired by the attribute information acquisition unit from a plurality of parameter sets that are setting information prepared in advance according to attributes of the container and the liquid; a parameter setting unit that sets the parameter set acquired by the parameter set acquisition unit as setting information for the inspection device; an inspection processing unit that controls an inspection process for detecting foreign matter contained in the liquid in the container based on the parameter set set by the parameter setting unit; a foreign object detection unit that performs a foreign object detection process to detect a pixel area having a predetermined area or larger in an image obtained by performing image processing on the captured image of the container as a foreign object in the liquid in the container; a parameter correction unit that corrects a parameter related to the erroneous detection of an air bubble in the liquid when the foreign object detection unit erroneously detects the air bubble as the foreign object; Equipped with The parameters related to the false detection of bubbles include a standard deviation of the size of the bubbles and an average brightness value of the bubbles, which are used in a masking process for masking the bubbles. Inspection equipment.

2. The inspection device according to claim 1 , wherein the parameter correction unit increases at least one of a standard deviation of the bubble sizes and an average brightness value of the bubbles.

3. The inspection device according to claim 1 , wherein the attributes of the container and the liquid include a type of shape of the container, a size of the container, a color of the container, and a color of the liquid.

4. The inspection device according to claim 1 , wherein the parameters related to erroneous detection of a foreign substance include a threshold that defines the predetermined area and a parameter related to the image processing.

5. 5. The inspection device according to claim 4, wherein the parameters relating to image processing include parameters relating to median filtering, binarization, and opening.

6. an image processing unit that performs the image processing on the captured image using the parameters related to the erroneous detection of the foreign matter corrected by the parameter correction unit to generate an image; the foreign object detection unit executes the foreign object detection process using the image generated by the image processing unit and the parameters related to the erroneous detection of the foreign object corrected by the parameter correction unit; 3. The inspection device according to claim 1, wherein the parameter correction unit corrects the parameter related to the erroneous detection of the foreign matter until the erroneous detection of the foreign matter by the foreign matter detection unit is eliminated.

7. The inspection equipment acquiring attribute information of the translucent container to be inspected and the liquid in the container; acquiring a parameter set corresponding to the acquired attribute information from a plurality of parameter sets which are setting information prepared in advance according to the attributes of the container and the liquid; The acquired parameter set is set as the setting information of the inspection device, performing a foreign object detection process for detecting a pixel area having a predetermined area or larger in an image obtained by performing image processing on the photographed image of the container as a foreign object in the liquid in the container; When an air bubble in the liquid is erroneously detected as the foreign object in the foreign object detection process, a parameter related to the erroneous detection of the air bubble is corrected; The parameters related to the false detection of bubbles include a standard deviation of the size of the bubbles and an average brightness value of the bubbles, which are used in a masking process for masking the bubbles. Parameter setting method.

8. For computers, acquiring attribute information of a translucent container to be inspected and a liquid in the container; acquiring a parameter set corresponding to the acquired attribute information from a plurality of parameter sets which are setting information prepared in advance according to the attributes of the container and the liquid; The acquired parameter set is set as the setting information of the inspection device, executing a foreign object detection process for detecting a pixel area having a predetermined area or larger in an image obtained by performing image processing on the photographed image of the container as a foreign object in the liquid in the container; When an air bubble in the liquid is erroneously detected as the foreign object in the foreign object detection process, a parameter related to the erroneous detection of the air bubble is corrected; The parameters related to the false detection of bubbles include a standard deviation of the size of the bubbles and an average brightness value of the bubbles, which are used in a masking process for masking the bubbles. Parameter setting program.

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