Glass bottle inspection method and glass bottle inspection device

Abstract

To provide a glass bottle inspection method that automatically determines the presence or absence of a defect with high inspection accuracy even in an area having a joint line.SOLUTION: An embodiment of a glass bottle inspection method includes: an image acquisition step of acquiring an image of a glass bottle; a line setting step of generating a line display image in which an identification line is arranged in the image to match an uneven portion derived from a mold shape of the glass bottle; a division step of dividing the image into a plurality of minute sections; an extraction step of extracting an image of interest by executing image processing for each minute section; an image generation step of generating an image for inspection by setting coordinates of the image of interest in the line display image when there is the image of interest in the extraction step; and a determination step of determining presence or absence of a defect by inputting the image for inspection to a learned model. The learned model performs machine learning using a learning image having no defect, a learning image having a defect, and a learning image having an identification line.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a glass bottle inspection method and a glass bottle inspection device. [Background technology]

[0002] A method for inspecting glass bottles with uneven engraving on the surface is proposed, for example, in Patent Document 1. The invention in Patent Document 1 proposes a process for masking the engraved area and a process for determining whether the area where the seam line appears is a defect.

[0003] In the invention of Patent Document 1, in order to distinguish between the detected object and the seam in the image, the influence of the seam is reduced by measuring the horizontal width and by image processing that eliminates vertical shadows. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 7220128 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the invention of Patent Document 1, the inspection area set in accordance with the seam and wider than the seam line inevitably has lower inspection accuracy than other inspection areas.

[0006] Therefore, the present invention provides a glass bottle inspection method and glass bottle inspection device that automatically determines the presence or absence of defects with high inspection accuracy even in areas where seams exist. [Means for solving the problem]

[0007] The present invention has been made to solve at least some of the above-mentioned problems, and can be realized as the following aspects or application examples.

[0008] [1] One aspect of the glass bottle inspection method of the present invention is to A method for inspecting glass bottles, comprising: an image acquisition step of acquiring an image of the glass bottle; a line setting step for generating a line display image in which identification lines are arranged on the image in accordance with the uneven portions resulting from the mold shape of the glass bottle; a segmentation step of segmenting the image into a plurality of micro-regions; an extraction step of extracting an image of interest by performing image processing for each of the minute sections; an image creation step of creating an inspection image by setting coordinates of the image of interest in the line display image when the image of interest is found in the extraction step; a determination step of inputting the inspection image into a trained model to determine whether or not there is a defect; Including, The trained model is characterized by being machine-learned using training images without defects, training images with defects, and training images with the discrimination line.

[0009] According to one aspect of the above-mentioned glass bottle inspection method, the presence or absence of defects can be determined using a trained model that uses training images with identification lines, making it possible to automatically determine the presence or absence of defects with high inspection accuracy even in areas with uneven parts such as seam lines.

[0010] [2] In one embodiment of the above-mentioned glass bottle inspection method, the extraction step includes a merging process for merging adjacent micro-sections to combine the target images; The image creating step can set the coordinates of the image of interest after the integration process in the line display image.

[0011] According to one aspect of the glass bottle inspection method, even if an image of interest spans multiple minute sections, the presence or absence of defects can be determined by integrating the image into a single image of interest.

[0012] [3] In one embodiment of the above-mentioned glass bottle inspection method, The learning images having the discrimination lines may include learning images having a plurality of types of discrimination lines with different thicknesses.

[0013] According to one aspect of the above-mentioned glass bottle inspection method, by using identification lines of different thicknesses, uneven parts can be judged as non-defective even when the judgment process is carried out using enlarged and / or reduced images.

[0014] [4] One aspect of the glass bottle inspection device of the present invention is: A glass bottle inspection device, a line setting unit that places an identification line on the image of the glass bottle in accordance with the uneven portion resulting from the mold shape; an image-of-interest extraction unit that divides the image into a plurality of minute sections and executes image processing for each minute section to extract an image of interest; an image creation unit that creates an inspection image by setting coordinates of the image of interest in the image in which the identification line is arranged; a determination unit that inputs the inspection image into a trained model and determines whether or not there is a defect; Including, The trained model is characterized by being machine-learned using training images without defects, training images with defects, and training images with the discrimination line.

[0015] According to one aspect of the above-mentioned glass bottle inspection device, the presence or absence of defects can be determined using a trained model that uses training images with identification lines, making it possible to automatically determine the presence or absence of defects with high inspection accuracy even in areas with uneven parts such as seam lines. [Effects of the Invention]

[0016] According to one aspect of the glass bottle inspection method and glass bottle inspection device of the present invention, it is possible to automatically determine the presence or absence of defects with high inspection accuracy even in areas with uneven parts such as seam lines. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a front view schematically showing an inspection device according to an embodiment of the present invention. [Figure 2] FIG. 1 is a diagram schematically illustrating an image of a glass bottle. [Figure 3] FIG. 10 is a diagram schematically illustrating an image on which identification lines are arranged. [Figure 4] FIG. 1 is a diagram illustrating a minute compartment. [Figure 5] FIG. 10 is a diagram showing a display unit that displays test results. [Figure 6] 1 is a flowchart of an inspection method according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0018] Preferred embodiments of the present invention will be described in detail below with reference to the drawings. Note that the embodiments described below do not unduly limit the content of the present invention as defined in the claims. Furthermore, not all of the configurations described below are necessarily essential components of the present invention. .

[0019] The glass bottle inspection method of this embodiment is a glass bottle inspection method that includes an image acquisition process for acquiring an image of the glass bottle, a line setting process for generating a line display image in which identification lines are arranged in the image to match the uneven portions resulting from the mold shape of the glass bottle, a division process for dividing the image into a plurality of tiny sections, an extraction process for performing image processing for each of the tiny sections to extract an image of interest, an image creation process for, if the image of interest is found in the extraction process, setting the coordinates of the image of interest in the line display image to create an inspection image, and a determination process for inputting the inspection image into a trained model and determining whether or not there is a defect, wherein the trained model is characterized by performing machine learning using training images without defects, training images with defects, and training images with the identification lines.

[0020] The glass bottle inspection device of this embodiment is a glass bottle inspection device that includes a line setting unit that places an identification line on an image of the glass bottle in accordance with the uneven parts resulting from the mold shape; an image of interest extraction unit that divides the image into a plurality of tiny sections and performs image processing for each of the tiny sections to extract an image of interest; an image creation unit that sets the coordinates of the image of interest on the image in which the identification line is placed to create an inspection image; and a judgment unit that inputs the inspection image into a trained model and judges whether or not there is a defect, and is characterized in that the trained model performs machine learning using training images without defects, training images with defects, and training images with the identification line.

[0021] 1. Inspection equipment The inspection device 100 for glass bottles 10 will be described in detail using Figures 1 to 5. Figure 1 is a front view that schematically shows the inspection device 100 according to this embodiment, Figure 2 is a diagram that schematically shows a first image 110 of the glass bottle 10, Figure 3 is a diagram that schematically shows a second image 112 in which the identification line 18a is arranged, Figure 4 is a diagram that explains the minute section A3, and Figure 5 is a diagram that shows the display unit 80 that displays the inspection results.

[0022] The inspection device 100 shown in Fig. 1 is an inspection device 100 for glass bottles 10. The inspection device 100 is incorporated as part of a manufacturing line for glass bottles 10 (not shown), and after molding, the glass bottles 10 that have been slowly cooled are transported to the inspection device 100, and after inspection, the glass bottles 10 are transported to the next process. The inspection device 100 may be equipped with multiple inspection stages, or may be installed on a conveyor.

[0023] The inspection device 100 includes a light-emitting unit 20 having a light-emitting surface 20a that irradiates light onto the glass bottle 10, an imaging unit 50 arranged opposite the light-emitting unit 20 across the glass bottle 10, and a control device 60 equipped with a judgment unit 64 that judges whether or not there is a defect based on, for example, a first image 110 (Figure 2) of the glass bottle 10 captured by the imaging unit 50.

[0024] As shown in Figure 1, the glass bottle 10 is inspected in an upright position, i.e., with its central axis 11 aligned vertically. The vertical direction is the direction of gravity, and the horizontal direction is the direction perpendicular to the vertical direction. Note that the central axis 11, indicated by the dashed line, is an imaginary line.

[0025] The inspection device 100 captures images of glass bottles 10 moving on a conveying path 40, such as a top chain conveyor, and inspects them sequentially. Glass bottles 10 are conveyed on the conveying path 40 with a gap between them and the glass bottles 10 in front and behind them. While the inspection device 100 is shown with one set of light-emitting unit 20 and image-capturing unit 50, multiple sets of light-emitting unit 20 and image-capturing unit 50 (not shown) may be provided so that the entire circumference of the glass bottle 10 can be imaged without omission. The inspection device 100 may also be configured to capture the entire circumference of the glass bottle 10 while rotating it around its central axis 11. In that case, a mounting table rotated by an electric motor may be provided instead of the conveying path 40.

[0026] The glass bottle 10 is, for example, transparent or translucent. Translucency refers to a degree of transparency that allows light from the light-emitting element 20 passing through the glass bottle 10 to detect defects 19a, 19b, such as surface bubbles, on the body 14 of the glass bottle 10. The glass bottle 10 has, for example, a mouth 12 with a circular cross section, a body 14, and a bottom 16. The body 14 includes a neck extending downward from the mouth 12 and a shoulder that gradually widens in diameter downward from the neck. The cross section of the glass bottle 10 may also be polygonal. The glass bottle 10 has uneven portions resulting from the mold shape. Such uneven portions include, for example, a seam line 18 formed at the joint of the mold, a recess defining the position for attaching a label, and patterns, letters, or other engravings carved into the mold.

[0027] The light-emitting unit 20 is a light source that illuminates the glass bottle 10. The light-emitting unit 20 has a light-emitting surface 20a on the side facing the glass bottle 10. The light-emitting unit 20 is a surface light source that can illuminate the glass bottle 10 from the opposite side of the imaging unit 50. The light-emitting surface 20a is, for example, rectangular in shape, and emits light from almost its entire surface. The light-emitting surface 20a is positioned approximately directly opposite the glass bottle 10 and the imaging unit 50 so that light that passes through the glass bottle 10 reaches the imaging unit 50. The light source for the light-emitting unit 20 can be a known light source, such as an LED or organic electroluminescence (EL). The light-emitting unit 20 is a diffuse light source. When an LED is used, a light-restricting unit 30 can be laminated on the light-restricting surface 20a to irradiate the glass bottle 10 with uniform light from the entire light-restricting surface 20a. The light-restricting unit 30 can be a known unit that suppresses the diffusion angle of light from a light source such as an LED and emits directional light to the outside. The light-limiting section 30 suppresses the diffusion angle of light, making it possible to capture images of, for example, wrinkles or streak shadows with a step depth of approximately 0.02 mm. The light-limiting section 30 can be formed by laminating a film that suppresses the diffusion angle in the horizontal direction and a film that suppresses the diffusion angle in the vertical direction. The light-limiting section 30 may also be formed by stacking multiple light control films, and when stacking films that suppress the diffusion angle in the same direction (for example, the horizontal direction), it is preferable to stack films set to different diffusion angles. Commercially available light control films can be used. Any film that can suppress the diffusion angle of light can be used for the light-limiting section 30, and is not limited to light control films.

[0028] The imaging unit 50 is positioned opposite the light-emitting unit 20 across the glass bottle 10. The imaging unit 50 is positioned so as to capture an image of the surface of the glass bottle 10 on an extension of the central axis 11. The imaging unit 50 preferably has a shallow depth of field so that the seam line 18 on the light-emitting unit 20 side is not captured. The imaging unit 50 may include multiple imaging units, such as a first imaging unit 51a and a second imaging unit 51b spaced apart vertically as shown in FIG. 1. The first imaging unit 51a and the second imaging unit 51b can capture at least the inspection target portion of the glass bottle 10 that is assigned to each imaging unit. For example, the first imaging unit 51a captures at least the upper region of the neck 12 and body 14 within its field of view. For example, the second imaging unit 51b captures at least the lower region of the body 14 within its field of view. The first and second imaging units 51a and 51b can capture a first image 110 (FIG. 2) including defects 19a and 19b using light from the light-emitting unit 20 that has passed through the glass bottle 10. The first and second imaging units 51a and 51b can be, for example, a known area sensor camera, or a line sensor camera that rotates the glass bottle 10 to capture an image. The imaging unit 50 transmits the captured image data to the control device 60.

[0029] The control device 60 includes a line setting unit 61, a target image extraction unit 62, an image creation unit 63, a determination unit 64, and an output unit 65. The control device 60 may further include a storage device (not shown). The control device 60 includes, for example, a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), a HDD (Hard Disk Drive), an SSD (Solid State Drive), a ROM (Read-Only Memory), a RAM (Random Access Memory), and the like. The control device 100 may also include a programmable logic controller (PLC), various network devices (e.g., switches, hubs, routers), and an image input board for transmitting captured image data. The inspection device 100 may further include a display unit 80, such as a liquid crystal display or organic electroluminescence (EL) display, that displays output from the control device 60. The control device 60 acquires image data from the imaging unit 50 and executes a process for inspecting the glass bottles 10. The process of transporting the glass bottles 10 at a predetermined speed along the transport path 40 may be executed by a control unit separate from the control device 60, or may be configured to be executed by the control device 60. The control device 60 can determine the timing of image capture based on signals from, for example, a transparent object detection sensor, a rotary encoder on the transport path 40, or a solid-state relay.

[0030] As shown in FIGS. 2 and 3, for example, the line setting unit 61 detects a glass bottle 10 in the first image 110 captured by the second imaging unit 51b and cuts out the inside of a first region A1, which is pre-prepared to correspond to, for example, the maximum width of the glass bottle 10, from the first image 110. The process of cutting out the first region A1 may be performed by a processing unit other than the line setting unit 61. The glass bottle 10 is detected by, for example, edge processing, detecting the shadow that appears at the boundary between the glass bottle 10 and the background. Since the glass bottle 10 to be inspected is predetermined, the size of the first region A1 can be stored in a storage device to match the outer shape of the glass bottle 10. By cutting out the first region A1, the background is reduced, as in the second image 112 of FIG. 3, thereby reducing the burden on the inspection device 100 in each process described below. Note that the same process as for the first image 110 can also be performed on the image captured by the first imaging unit 51a.

[0031] As shown in FIG. 3, the line setting unit 61 places an identification line 18a in the second image 112 of the glass bottle 10, aligned with uneven portions resulting from the mold shape, such as the seam line 18. In FIG. 3, the glass bottle 10 is depicted in black to show the identification line 18a as a white line. The seam line 18 forms a step on the surface of the glass bottle 10 along the mold seam, and a portion of it may appear as a dark line in the first image 110 (FIG. 2). For example, if a portion of the seam line 18 can be detected in the straight portion of the body 14, the entire seam line 18 can be predicted based on shape information about the glass bottle 10, such as the body diameter. Because the seam line 18 appears as a straight line in a planar view, it is easy to predict the entire seam line 18. The identification line 18a is generated according to the coordinates of the predicted seam line 18. The seam line 18 can be detected, for example, by setting a rectangular edge detection area in the center of the straight portion of the body 14 in the first image 110 and performing edge detection processing to find vertical lines. Information about the body diameter of glass bottle 10 can be detected, for example, by edge detection processing on both the left and right sides of first image 110. In this embodiment, seam line 18 is described, but similar processing can be used for other uneven parts. In the case of uneven parts derived from the mold shape other than seam line 18, for example, coordinate information for the uneven parts on glass bottle 10 can be estimated or acquired, and an identification line 18a artificially created in advance and stored in a memory device can be placed in second image 112 in accordance with that coordinate information.

[0032] As shown in FIG. 4, the target image extraction unit 62 may set a second area A2 (area surrounded by a dashed line) to be inspected in the second image 112. The second area A2 in the second image 112 captured by the second imaging unit 51b is set, for example, from the center to the lower half of the body 14 of the glass bottle 10. In this embodiment, there are images captured by the first imaging unit 51a and images captured by the second imaging unit 51b, so the area to be inspected can be set according to the range in which high inspection accuracy is expected for each image. To explain the size of the second area A2, the second area A2 is shown on the right side of FIG. 4. The target image extraction unit 62 divides the second image 112 into a plurality of minute sections A3 and performs image processing for each minute section A3 to extract the target image 114. The minute section A3 is a section surrounded by a dotted line in FIG. 3 and is set, for example, within the second area A2. To explain one unit of the minute division A3, one unit of the minute division A3 is shown in the upper left of Figure 4. In Figure 4, the minute divisions A3 divide the second area A2 into a grid pattern, but this is not limited to this, and the minute divisions A3 may be divided so that they partially overlap with adjacent minute divisions A3. The size of the minute division A3 can be set according to the size of the target defect, and can be set, for example, to be slightly larger than the target defect.

[0033] The target image extraction unit 62 can reduce processing time by performing image processing for each minute section A3 using parallel processing. Image processing can be performed using a combination of multiple image processing algorithms and parameters used for each algorithm, which can detect the target defects 19a and 19b as points of interest 115. The points of interest 115 are singular points detected by image processing, and include the defects 19a and 19b. The minute section A3 containing the points of interest 115 is the target image 114. An example of the target image 114 after image processing is shown on the left side of Figure 4. Image processing algorithms include, for example, various filter processes for noise removal, correction processes such as brightness correction, binarization processes, edge detection processes, frequency filter processes, arithmetic operations such as arithmetic operations, morphology processes, detection target determination processes, and branching processes.

[0034] Image processing for each micro-section A3 can be optimized using, for example, evolutionary computation using the GNP (Genetic Network Programming) method. Regarding the GNP method, for example, the method disclosed in Japanese Patent Application Laid-Open No. 2022-89430, which optimizes points of interest 115 in an image using a discrete optimization algorithm, can be applied. As the discrete optimization algorithm, evolutionary computation, which uses genetic operations for optimization, is preferably used, but other algorithms may be used as long as they achieve similar results. Image processing for each micro-section A3 can be performed by a detector that has been optimized in advance using training information including training images divided in the same manner as the micro-section A3 and individual information (image processing algorithm). The training images include, for example, the shapes, number, positions, and ranges of multiple types of points of interest 115. For example, an image showing the points of interest 115 in white on a black background can be used. Optimization can be performed by repeating generations through, for example, crossover, mating, and mutation, where an input image is input to the individual information, and individual information that produces an evaluation value close to that of the teacher image remains, thereby generating an optimized detector. By performing image processing for each micro-section A3 using the optimized detector, it is possible to detect and extract an image of interest 114 that includes a desired point of interest 115.

[0035] The image-of-interest extraction unit 62 can execute a merging process to merge adjacent minute sections A3 to combine the image of interest 114. For example, if one defect 19a shown in FIG. 4 is divided into two minute sections A3, the merging process can be executed to merge them into one merged section A4, and the original image of interest 114 can be extracted.

[0036] The image creation unit 63 sets the coordinates of the image of interest 114 in the second image 112 (FIG. 3) in which the identification line 18a is arranged, to create the test image 113 (FIG. 5). The identification line 18a in FIG. 5 is depicted as a black line for ease of explanation, but it can also be a white line. When multiple micro-sections A3 are integrated into an integrated section A4, the image creation unit 63 can set the coordinates of the image of interest 114 after the integration process in the second image 112. The image creation unit 63 may also set information such as the shape, size, and brightness of the point of interest 115 in addition to the coordinates in the image of interest 114 of the test image 113. Note that FIG. 5 shows the state in which the indication unit 90 and the result display unit 92 are added to the test image 113 and displayed on the display unit 80.

[0037] The judgment unit 64 inputs the inspection image 113 into the trained model to judge the presence or absence of defects 19a, 19b. For example, the coordinates of a plurality of target images 114 are set in the inspection image 113, and therefore judgment processing is performed for the image at those coordinates using the trained model. The judgment processing may be performed in parallel for the coordinates of a plurality of target images 114. The trained model is used to judge the presence or absence of defects. Machine learning is performed using training images without defects, training images with defects, and training images with the discrimination line 18a. The training images with the discrimination line 18a can include images with defects and images without defects, and training images without defects but with the discrimination line 18a can be trained as non-defective products. The discrimination line 18a is preferably thinner than the defects 19a and 19b so that the target defects 19a and 19b are not hidden by the discrimination line 18a. Using training images with the discrimination line 18a makes it less likely that the trained model will erroneously determine the discrimination line 18a as a defect. The training images with the discrimination line 18a can include training images with multiple types of discrimination lines 18a of different thicknesses. By training with training images with discrimination lines 18a of different thicknesses, the judgment unit 64 is less likely to determine the discrimination line 18a as a defect even when it cuts out the focus point 115 of the inspection image 113 and enlarges or reduces the image for judgment. The trained model can be trained using machine learning using a neural network. As the neural network, it is preferable to use a convolutional neural network (CNN) with a convolutional layer. Alternatively, for example, a generative adversarial network (GAN) disclosed in Japanese Patent Application Laid-Open Publication No. 2021-89219 may be used to generate artificial images, which may then be used as training images. The types of defects in the training images include, for example, streaks, wrinkles, transparent stones, cat scratches, bubbles, and surface bubbles, but other types of defects may be added depending on the inspection requirements.

[0038] The control device 60 outputs the determination result of the determination unit 64 to the outside, and can display the inspection result together with the inspection image 113 on the display unit 80, for example, as shown in FIG. 5. The inspection device 100 can also reject glass bottles 10 determined to have defects, for example, on a line subsequent to a discharge unit (not shown). The display unit 80 can display a designation unit 90 on the portion of the inspection image 113 determined to have defects 19a, 19b, and can display a result display unit 92 that displays information about the defects 19a, 19b. The result display unit 92 can display the type of defect 19a and the probability that it is of that type, or it can simply display whether or not it is a defect 19a.

[0039] According to the inspection device 100, the presence or absence of defects 19a and 19b can be determined using a trained model that uses a training image with an identification line 18a, so that the presence or absence of defects 19a and 19b can be automatically determined with high inspection accuracy even in areas with uneven portions such as the seam line 18.

[0040] 2. Inspection method An inspection method for glass bottles 10 according to this embodiment using inspection device 100 in Fig. 1 will be described with reference to Fig. 1 to Fig. 6. Fig. 6 is a flowchart of the inspection method according to this embodiment.

[0041] As shown in Figure 6, the inspection method according to this embodiment is a method for inspecting glass bottles 10 and includes at least an image acquisition step S10, a line setting step S32, a division step S14, an extraction step S20, an image creation step S22, and a judgment step S24. The inspection method according to this embodiment may further include a trimming step S12 after S10, and the extraction step S20 may further include an image processing step S16 and an integration step S18, and may further include a step S26 of displaying the judgment results after S24. Each step will be explained in order below with reference to Figures 1 to 5. Note that the following explanation will omit parts that overlap with the above explanation of the inspection device 100.

[0042] S10: The control device 60 executes an image acquisition step to acquire a first image 110 (FIG. 2) of the glass bottle 10. Specifically, when the glass bottle 10 is transported to a predetermined position by the transport path 40, the control device 60 commands the image capture unit 50 to start capturing an image, and acquires the first image 110 output from the image capture unit 50. The first image 110 is stored in a storage device (not shown) of the control device 60. will be done.

[0043] S12: The control device 60 executes a trimming process to cut out a first area A1 (shown by a dashed line in FIG. 2) from the first image 110, which is adjusted to the maximum width of the glass bottle 10. The second image 112 cut out in S12 has a smaller background area, as shown in FIGS. 3 and 4, which reduces the burden of each process. The control device 60 can process the flows of S14, S16, S18, and S20 and the flows of S30 and S32 in parallel for the second image 112 cut out in S12.

[0044] S14: For example, the target image extraction unit 62 of the control device 60 executes a division step of dividing the second image 112 into a plurality of minute sections A3 (FIG. 4). In FIG. 4, the second area A2 to be inspected is divided into a plurality of minute sections A3. The minute sections A3 have a predetermined size.

[0045] S16: For example, the target image extraction unit 62 of the control device 60 executes an image processing step that executes image processing for each minute section A3. By executing image processing for each minute section A3, the control device 60 can perform high-speed processing, and each image processing can be performed in parallel. The image processing executes an image processing algorithm that clearly extracts the target point 115 within the minute section A3. The target image extraction unit 62 may execute image processing for each minute section A3 using the optimized detector described above as the image processing algorithm. By executing image processing for each minute section A3, the influence of the outer shape of the glass bottle 10 on the image processing can be reduced. If the influence of the outer shape of the glass bottle 10 is less, the detector that detects the target image 114 can be more generalized, and the same detector can be applied to glass bottles of other shapes.

[0046] S18: For example, the target image extraction unit 62 of the control device 60 executes an integration process step of integrating adjacent minute sections A3 to combine the target image 114. The integrated target image 114 becomes a single target point 115 before division. Even if the target image 114 spans multiple minute sections A3, the process of integrating them into a single integrated section A4 allows the presence or absence of defects 19a, 19b to be determined as a single target image 114 in S24, which will be described later.

[0047] S20: The control device 60, for example, the image of interest extraction unit 62, executes an extraction step to extract the image of interest 114. The image of interest extraction unit 62 determines whether or not there are image-processed and / or integrated images of interest 114 for all micro-sections A3. The extraction of the image of interest 114 can be performed, for example, by labeling and blob analysis. The image of interest extraction unit 62 labels the image of interest 114 and can output the coordinates, size, aspect ratio, etc. of the point of interest 115. After image processing, the image of interest 114 clearly expresses the point of interest 115 as a cluster. As shown on the left side of Figure 4, the image of interest 114 may have, for example, a black background against which a cluster of white points of interest 115 is formed. If there are no images of interest 114 in all micro-sections A3 (NO), the control device 60 executes S26. If there are images of interest 114 in all micro-sections A3 (YES), the control device 60 executes S22.

[0048] S30: The line setting unit 61 of the control device 60, for example, determines whether or not any uneven portions resulting from the mold shape of the glass bottle 10 have been detected in the second image 112. The uneven portions are detected, for example, by subjecting the second image 112 to a known method, such as edge detection processing. The uneven portions, for example, the seam line 18, can be detected as lines extending vertically to the body 14. If no uneven portions are detected in the second image 112 (NO), the control device 60 executes S22. If an uneven portion is detected in the second image 112 (YES), the line setting unit 61 executes S32.

[0049] S32: For example, the line setting unit 61 of the control device 60 sets the second image 11 in accordance with the uneven portion. A line setting process is performed to generate a line display image in which identification lines 18a are arranged in the second image 112. Figure 3 shows the second image 112 in which the identification lines 18a are arranged. Since the unevenness is due to the shape of the mold, if a portion of the unevenness can be detected, the entire unevenness can be predicted based on other shape information of the glass bottle 10. If an unevenness, such as a portion of the seam line 18, can be detected in the straight portion of the body 14, the entire seam line 18 can be predicted based on information about the outer diameter of the glass bottle 10. Information about the outer diameter of the glass bottle 10 can be detected, for example, using edge detection processing. A line display image can be generated by arranging an identification line 18a in the second image 112 that matches the coordinates of the unevenness detected in S30. The identification line 18a may be generated for each image to match the detected unevenness, or an appropriate identification line 18a may be selected and arranged from multiple identification lines 18a previously generated and stored to match the detected unevenness. The identification line 18a may be a white line. The identification line 18a is not a mask for hiding the shadow of the uneven portion, and therefore may be thinner than, for example, the seam line 18 that appears in the second image 112.

[0050] S22: For example, the image creation unit 63 of the control device 60 sets the coordinates of the image of interest 114 in the line display image (second image 112 in FIG. 3) to create an inspection image 113 (FIG. 5). Using the minute section A3 allows the image of interest 114 to be extracted efficiently, but dividing the seam line 18 makes it difficult to detect the seam line 18. For this reason, it is preferable to execute S32 in a flow separate from S14 to S20. If there is an image of interest 114 integrated in S18, the image creation unit 63 can set the coordinates of the image of interest 114 after the integration process in the line display image.

[0051] S24: For example, the determination unit 64 of the control device 60 executes a determination step in which the inspection image 113 is input into a trained model to determine the presence or absence of defects 19a and 19b. The trained model can be the trained model described for the inspection device 100. The trained model is machine-learned using training images without defects, training images with defects, and training images with the discrimination line 18a. Training images with the discrimination line 18a can include images with and without defects. By training training images without defects and training images with the discrimination line 18a as being without defects, even if the shadow of the seam line 18 is extracted as the focus point 115 in S20, if there is no focus point 115 other than the seam line 18, it can be determined that there is no defect at that coordinate. Furthermore, by including training images with the discrimination line 18a as images with and without defects, it can be determined that there is a defect if there is a defect 19a or 19b near the seam line 18. In this way, the presence or absence of defects 19a and 19b can be determined using a trained model that uses training images with discrimination lines 18a, so that the presence or absence of defects 19a and 19b can be automatically determined with high inspection accuracy even in areas with uneven portions such as seam lines 18. Training images with discrimination lines 18a used in the trained model can include training images in which multiple types of discrimination lines 18a with different thicknesses are set. By using discrimination lines 18a with different thicknesses, uneven portions can be determined to be non-defective even when S24 is performed on an enlarged and / or reduced image.

[0052] The control device 60 may additionally train the trained model using rejected images of non-defective products. The rejected images of non-defective products can be test images 113, which are glass bottles 10 that have been determined to have defects 19a and 19b in S24 and that an operator visually inspects and determines to be non-defective. Since the test images 113 contain information about the coordinates and size of the focus point 115, the control device 60 automatically extracts an image including the focus point 115 from the test images 113 and stores it in a training image folder as a defect-free training image. The trained model is then additionally trained using the additionally stored defect-free training image. This allows the trained model to accurately determine that a product is non-defective, even if the focus point 115 is likely to be determined to be a defect 19a or 19b.

[0053] S26: For example, the output unit 65 of the control device 60 can display the judgment result of the judgment unit 64 on the display unit 80 together with the inspection image 113 as shown in FIG.

[0054] The present invention is not limited to the above-described embodiments, and various modifications are possible, including configurations that are substantially identical to the configurations described in the embodiments. Here, "same configuration" means a configuration that has the same function, method, and result, or a configuration that has the same purpose and effect. The present invention also includes configurations in which non-essential parts of the configurations described in the embodiments are replaced. The present invention also includes configurations that achieve the same effects or purposes as the configurations described in the embodiments. The present invention also includes configurations in which publicly known technology is added to the configurations described in the embodiments. [Explanation of symbols]

[0055] 10...glass bottle, 11...center axis, 12...mouth, 14...body, 16...bottom, 18...seam line, 18a...identification line, 19a, 19b...defect, 20...light emitting section, 20a...light emitting surface, 30...light restricting section, 40...conveying path, 50...imaging section, 51a...first imaging section, 51b...second imaging section, 60...controller, 61...line setting section, 62...target image extraction section, 63...image creation section, 64...judgment section, 65...output section, 80...display section, 90...display section, 92...result display section, 100...inspection device, 110...first image, 112...second image, 113...inspection image, 114...target image, 115...target point, A1...first region, A2...second region, A3...microsection, A4...integrated section

Claims

1. A method for inspecting glass bottles, comprising: an image acquisition step of acquiring an image of the glass bottle; a line setting step for generating a line display image in which identification lines are arranged on the image in accordance with the uneven portions resulting from the mold shape of the glass bottle; a segmentation step of segmenting the image into a plurality of micro-regions; an extraction step of extracting an image of interest by performing image processing for each of the minute sections; an image creation step of creating an inspection image by setting coordinates of the image of interest in the line display image when the image of interest is found in the extraction step; a determination step of inputting the inspection image into a trained model to determine whether or not there is a defect; Including, A glass bottle inspection method characterized in that the trained model is trained by machine learning using training images without defects, training images with defects, and training images with the identification line.

2. The glass bottle inspection method according to claim 1, the extraction step includes a merging process for merging adjacent micro-sections to combine the target images; A glass bottle inspection method, characterized in that the image creation step sets the coordinates of the image of interest after the integration process in the line display image.

3. The glass bottle inspection method according to claim 1 or 2, A glass bottle inspection method, characterized in that the learning images with the identification lines include learning images on which multiple types of identification lines with different thicknesses are set.

4. A glass bottle inspection device, a line setting unit that places an identification line on the image of the glass bottle in accordance with the uneven portion resulting from the mold shape; an image-of-interest extraction unit that divides the image into a plurality of minute sections and executes image processing for each minute section to extract an image of interest; an image creation unit that creates an inspection image by setting coordinates of the image of interest in the image in which the identification line is arranged; a determination unit that inputs the inspection image into a trained model and determines whether or not there is a defect; Including, A glass bottle inspection device characterized in that the trained model is trained by machine learning using training images without defects, training images with defects, and training images with the identification line.

Images