Patent Publication Number: US-2021166374-A1

Title: Construction method, inspection method, and program for image data with label, and construction device and inspection device for image data with label

Description:
TECHNICAL FIELD 
     The present invention relates to a method for generating labeled image data, which is provided as teaching data to an identifier when performing supervised learning and the like, a method for inspection, a program, a labeled image data generation device, and an inspection device. 
     BACKGROUND ART 
     Patent Document 1 discloses an example of a learning device that divides image data including a correct label into pieces of data, performs additional learning, and adds stable outcomes of the additional learning to supervised images. The learning device includes an image generation unit, an image selection unit, an output unit, and a correct label setting unit. The image generation unit generates an unlabeled image based on a first image including a correct label. The image selection unit selects and presents an unlabeled image to a user. The output unit outputs the selected unlabeled image on a display. The correct label setting unit associates a correct label, which is obtained from an input unit, with the selected unlabeled image to set a second image including a correct label. A classifier generation unit performs a regeneration process of a classifier based on a feature vector of the first image including a correct label and a feature vector of the second image including a correct label. 
     Patent Document 2 discloses a machine learning device that generates teaching data through machine learning. 
     PRIOR ART DOCUMENTS 
     Patent Documents 
     Patent Document 1: Japanese Laid-Open Patent Publication No. 2013-125322 
     Patent Document 2: Japanese Laid-Open Patent Publication No. 2015-185149 
     SUMMARY OF THE INVENTION 
     Problem that the Invention is to Solve 
     In machine learning such as deep learning, a massive number of images need to be learned to determine a parameter of an identifier. For example, when an inspection is performed using an identifier, precision is improved if the image is divided as described in Patent Document 1. In this case, a label needs to be assigned to each image segment. Also, when an accuracy rate of the identifier is not sufficient, an identifier with a higher accuracy rate may be obtained by changing the number of segments of the image and performing the learning again. 
     Nonetheless, when the number of segments of an image is changed, a label needs to be reassigned to each image segment of the image for which the number of segments was changed. This type of problem is not limited to deep learning and widely occurs in supervised machine learning performed by a support vector machine (SVM) described in Patent Document 1 or the like. 
     The objective of the present invention is to provide a method for generating labeled image data, a method for inspection, a program, a labeled image data generation device, and a labeled image data inspection device that facilitate generation of labeled image data, which is generated by dividing images to be used as teaching data in supervised or semi-supervised machine learning, and reduce the cost of learning. 
     Means for Solving the Problem 
     Means and operational advantages for solving the above-described problem will now be described. 
     In a method for generating labeled image data that solves the above problem, the labeled image data is used as teaching data for having a pre-learning identifier perform supervised or semi-supervised machine learning to generate an identifier used for inspecting an inspection subject, of which an image is captured, for defects. The method includes a channel addition step, a pixel value assignment step, a segmentation step, a label assignment step, and a channel removal step. In the channel addition step, in addition to one or more channels forming the image, an other channel is added to the captured image of the inspection subject. In the pixel value assignment step, a second pixel value is assigned to a region corresponding to a defect region in the image in the other channel. The second pixel value differs from a first pixel value, which is assigned to an other region that is defect-free, as a pixel value indicating a defect. In the segmentation step, the image including the other channel is segmented into multiple image segments. In the label assignment step, a defect label is assigned to an image segment of the image segments, in which the second pixel value is included in the other channel, and a non-defect label is assigned to an image segment of the image segments, in which the second pixel value is not included in the other channel. In the channel removal step, the other channel is removed from the image segment to generate labeled image data. 
     With this method, even when the labeled image data used as teaching data is generated by dividing the captured image, the generation process is facilitated and the cost of learning is reduced. For example, even when the number of segments of the image is changed for regeneration of the labeled image data, a technician does not need to respecify the defect region in the image whenever the number of segments is changed. This facilitates the process and reduces the learning cost. 
     In the method for generating labeled image data, it is preferred that there be multiple types of the defect. Preferably, in the pixel value assignment step, when assigning the second pixel value to the other channel, a different second pixel value is assigned in accordance with the type of the defect. Further, it is preferred that in the label assignment step, a different defect label be assigned to the image segment including the second pixel value in accordance with the value of the second pixel value. 
     This method allows the labeled image data to be generated including a defect label that differs in accordance with the type of the defect. 
     In the method for generating labeled image data, it is preferred that in the segmentation step, the image including the other channel be segmented into the multiple image segments in which adjacent image segments are partially overlapped with each other. 
     With this method, even when the defect is shown over multiple image segments, the defect is split less frequently. Thus, a high accuracy rate is readily obtained when learning is performed using the labeled image data. 
     In the method for generating labeled image data, it is preferred that the other channel be an alpha channel. 
     With this method, the use of the alpha channel that sets the opacity of an image allows the second pixel value indicating a defect to be assigned to the region corresponds to the defect region relatively easily without affecting the channel of the captured image. 
     In the method for generating labeled image data, it is preferred that if an accuracy rate greater than or equal to a predetermined value is not obtained from an output when test data is input to the identifier generated by performing supervised or semi-supervised machine learning using the labeled image data as teaching data, the number of segments in the segmentation step be changed. 
     With this method, the image already includes the other channel and the second pixel value indicating a defect if an accuracy rate greater than or equal to a predetermined value is not obtained from the output when test data is input to the identifier, which is generated by performing supervised or semi-supervised machine learning, and the number of segments is changed to regenerate the labeled image data. Thus, when the number of segments of the image is changed to regenerate the labeled image data, there is no need for a technician to operate the input device to specify the defect region so that the region is specified and the second pixel value is assigned in the other channel. 
     A method for inspection that solves the above problem includes an identifier generation step and a determination step. In the identifier generation step, an identifier is generated by performing supervised or semi-supervised machine learning using labeled image data as teaching data, the labeled image data being generated by the method for generating labeled image data. In the determination step, multiple image segments are obtained by segmenting an image of an inspection subject into the number of segments that is the same as the number of segments of the image segments used for generating the identifier. Further, in the determination step, whether the inspection subject is defective is determined based on an output value output from the identifier as an identification result when the image segments are input to the identifier. 
     The method facilitates generation of the labeled image data used for learning of the pre-learning identifier thereby allowing the inspection subject to be inspected for defects at a lower learning cost. 
     In the method for inspection, it is preferred that the machine learning be deep learning. 
     This method allows the inspection subject to be inspected for defects at a lower learning cost through deep learning. 
     A program that solves the above problem is executed by a computer that generates labeled image data used as teaching data for having a pre-learning identifier perform supervised or semi-supervised machine learning to generate an identifier used for inspecting an inspection subject, of which an image is captured, for defects. The program includes a channel addition step, a pixel value assignment step, a segmentation step, a label assignment step, and a channel removal step. In the channel addition step, the computer adds, in addition to one or more channels forming the image, an other channel to the captured image of the inspection subject. In the pixel value assignment step, the computer assigns a second pixel value to a region corresponding to a defect region in the image in the other channel. The second pixel value differs from a first pixel value, which is assigned to an other region that is defect-free, as a pixel value indicating a defect. In the segmentation step, the computer segments the image including the other channel into multiple image segments. In the label assignment step, the computer assigns a defect label to an image segment of the image segments, in which the second pixel value is included in the other channel, and assigns a non-defect label to an image segment of the image segments, in which the second pixel value is not included in the other channel. In the channel removal step, the computer removes the other channel from the image segment to generate labeled image data. 
     The program is executed by the computer so that the generation process is facilitated for the labeled image data, which is generated by dividing the captured image and regenerated if the accuracy rate of the identifier that has completed learning is low by changing the number of segments, and that the learning cost is reduced. For example, even when the number of segments of the image is changed for regeneration of the labeled image data, a technician does not need to respecify the defect region in the image whenever the number of segments is changed. This facilitates the process and reduces the learning cost. 
     In a labeled image data generation device that solves the above program, the labeled image data is used as teaching data for having a pre-learning identifier perform supervised or semi-supervised machine learning to generate an identifier used for inspecting an inspection subject, of which an image is captured, for defects. The device includes a channel addition unit, a pixel value assignment unit, a segmentation unit, and a label assignment unit. The channel addition unit adds, in addition to one or more channels forming the image, an other channel to the captured image of the inspection subject. The pixel value assignment unit assigns a second pixel value to a region corresponding to a defect region in the image in the other channel. The second pixel value differs from a first pixel value, which is assigned to an other region that is defect-free, as a pixel value indicating a defect. The segmentation unit divides the image including the other channel into multiple image segments. The label assignment unit assigns a defect label to an image segment of the image segments, in which the second pixel value is included in the other channel, and assigns a non-defect label to an image segment of the image segments, in which the second pixel value is not included in the other channel. The label assignment unit further removes the other channel from the image segment after the label is assigned to generate labeled image data. 
     The labeled image data generation device facilitates generation of the labeled image data, which is generated by dividing the captured image and regenerated if the accuracy rate of the identifier that has complete learning is low by changing the number of segments, and lowers the learning cost. For example, even when the number of segments of the image is changed for regeneration of the labeled image data, a technician does not need to respecify the defect region in the image whenever the number of segments is changed. This facilitates the process and reduces the learning cost. 
     An inspection device that solves the above problem includes the labeled image data generation device, an identifier, an image segment acquisition unit, and a determination unit. The identifier is generated by performing supervised or semi-supervised machine learning using the labeled image data, which is generated by the labeled image data generation device, as teaching data. The image segment acquisition unit obtains multiple image segments by dividing an image of an inspection subject into the number of segments that is the same as the number of segments of the image segments used for generating the identifier. The determination unit determines whether the inspection subject is defective based on an output result from the identifier when the image segments obtained by the image segment acquisition unit is input to the identifier. 
     The inspection device facilitates generation of the labeled image data used for learning by the identifier that has not completed learning. Thus, the inspection subject can be inspected for defects at a lower learning cost. 
     Effects of the Invention 
     The present invention facilitates the generation process of the labeled image data even when the labeled image data, which is used as the teaching data in supervised or semi-supervised machine learning, is generated by dividing images and thus the learning cost is reduced. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic side view showing an inspection device that has a machine learning functionality in accordance with a first embodiment. 
         FIG. 2  is a schematic plan view showing a portion of the inspection device where an image of an item, which is an inspection subject, is captured. 
         FIG. 3  is schematic diagram showing one example of multiple types of folders and images in the folders. 
         FIG. 4  is a block diagram showing the electrical configuration of the inspection device having the machine learning functionality. 
         FIG. 5A  is a schematic diagram showing a captured image, and  FIG. 5B  is a schematic diagram showing the channel structure of the captured image. 
         FIG. 6A  is a schematic diagram showing a defect in the image, and  FIG. 6B  is a schematic diagram illustrating a method for specifying and inputting a defect region. 
         FIG. 7  is a schematic diagram showing the structure of an image including an alpha channel. 
         FIG. 8  is a schematic diagram illustrating segmentation of an image. 
         FIGS. 9A and 9B  are schematic diagrams illustrating how to divide an image. 
         FIG. 10A  is a schematic diagram illustrating a label setting,  FIG. 10B  is a schematic diagram showing labeled image segment data as teaching data, and  FIG. 10C  is a schematic diagram illustrating an array data conversion. 
         FIG. 11  is a schematic diagram illustrating supervised learning of an identifier in a deep-learning unit. 
         FIG. 12  is a flowchart illustrating a defect position registration routine. 
         FIG. 13  is a flowchart illustrating an image segment generation routine in which labeled image segments are generated as teaching data. 
         FIGS. 14A and 14B  are schematic diagrams illustrating a process for assigning a second pixel value to a defect region in the alpha channel in accordance with a second embodiment. 
         FIG. 15  is a flowchart illustrating a main part of an image segment generation routine in which labeled image segments are generated as teaching data. 
     
    
    
     MODES FOR CARRYING OUT THE INVENTION 
     First Embodiment 
     An inspection system including an inspection device that has a machine learning functionality will now be described with reference to the drawings. An inspection system  11  shown in  FIG. 1  inspects an item  12  for defects using an image of the item  12 . The inspection system  11  includes a transfer device  20  and an inspection device  30 . The transfer device  20  transfers the item  12 , and the inspection device  30  inspects the item  12  transferred by the transfer device  20 . 
     As shown in  FIG. 1 , the transfer device  20  includes a conveyor  21 , a sensor  22 , and a discarding device  23 . The conveyor  21  conveys the item  12 , and the sensor  22  detects the item  12  conveyed on the conveyor  21 . When the item  12  is determined to be defective based on an inspection result of the inspection device  30 , the discarding device  23  discards the defective item  12  from the production line of non-defective items. The conveyor  21  is, for example, a belt conveyor, in which a belt  25  running around rollers  24  is moved by the power of an electric motor  26 . The conveyor  21  may have a different construction such as that of a roller conveyor as long as the item  12  can be conveyed. Two guide members  27  are arranged at two sides of the conveyor  21  in a widthwise direction that intersects a transfer direction X to guide the item  12 . The discarding device  23  may be configured to discard the item  12  by pushing out the item  12  or blowing out the item  12  with air. The discarding device  23  may have any construction as long as the defective item  12  can be discarded. 
     The inspection system  11  includes a controller C that controls the transfer device  20  and the inspection device  30 . 
     The inspection device  30  includes a camera  31  and a computer  32 . The camera  31  captures an image of the item  12  conveyed by the conveyor  21 , and the computer  32  is electrically connected to the camera  31 . The inspection device  30  determines whether the item  12  is defective based on the image of the item  12  captured by the camera  31 . The computer  32  forms part of the controller C. Specifically, the controller C includes a programmable logic controller  33  (hereafter, also referred to as “PLC  33 ”) and the computer  32 . The PLC  33  controls the transfer device  20  and the camera  31 , and the computer  32  is electrically connected to the PLC  33 . The computer  32  includes an input device  34  and a display  35 . 
     The PLC  33  controls the transfer device  20  to control the image-capturing timing of the camera  31  based on a detection signal that indicates detection of the item  12  by the sensor  22 . The image data of the item  12  captured by the camera  31  is sent to the computer  32 . The computer  32  inspects the item  12  for defects using the image data sent from the camera  31 . In the example shown in  FIG. 1 , the computer  32  is a personal computer or the like. Alternatively, the computer  32  may be a microprocessor or the like that is incorporated in the controller C, in which the computer  32  and the PLC  33  are integrated. 
     In  FIGS. 1 and 2 , the example of the item  12  has the form of a rectangular plate. However, the item  12  may be any item having any shape and size. 
     As shown in  FIG. 5A , in addition to an item  12   g , the image captured by the camera  31  includes a belt  25   g  and part of guide members  27   g  located near the item  12   g  as part of the image. The inspection device  30  in the present embodiment inspects the item  12  for defects using an identifier  66  (refer to  FIG. 4 ) based on the image data captured by the camera  31 . When a defect is detected, the inspection device  30  also determines the type of the defect. 
     In the present embodiment, the inspection device  30  performs supervised learning to generate the identifier  66  required for the inspection. In the present example, deep learning is employed as machine learning. In deep learning, a large number of images need to be learned to obtain the identifier  66  with a high accuracy rate. However, it is troublesome to prepare a large number of training images. Accordingly, generation of labeled image data is simplified and modified so that regeneration of labeled image data resulting from a change in a condition will be facilitated. 
     The inspection device  30  generates, for example, labeled image segment data DIm (refer to  FIG. 10B ) as teaching data, which is a set of image segment data and a correct label. The image segment data is obtained by dividing the image of the item  12  captured by the camera  31 . The correct label indicates whether the item  12  is defective and the type of the defect when the item  12  is defective. Then, the inspection device  30  performs supervised learning using the labeled image segment data DIm to generate the identifier  66 . 
     As shown in  FIG. 3 , image (original image) data used to generate teaching data is classified and stored in non-defective item folder F 1  and defective item folders F 2  and F 3 . The non-defective item folder F 1  stores image data Im 1  of non-defective items. Multiple types (two in example of  FIG. 3 ) of the defective item folders F 2  and F 3  are prepared in correspondence with the types of defects. For example, the folder F 2  stores image data Im 2  of defective items having small defects Ds. Further, the folder F 3  stores image data Im 3  of defective items having large defects Dl. When the image data Im 2  and Im 3  is not particularly classified into the types of defects, the image data Im 2  and Im 3  may collectively be referred to as image data Imd. Also, the original images used to generate teaching data are not limited to the ones captured by the camera  31  of the inspection device  30  and may be images of the item  12  captured in advance at a different location by a camera that is separate from the inspection device  30 . In this case, the image data may be input to the computer  32  from an external memory. Alternatively, the computer  32  may receive the image data via the internet from a server. 
     The electrical configuration of the inspection system  11  will now be described with reference to  FIG. 4 . As shown in  FIG. 4 , the controller C includes the computer  32  and the PLC  33 . The conveyor  21 , the sensor  22 , and the discarding device  23 , which form the transfer device  20 , are electrically connected to the PLC  33 . The PLC  33  includes a camera trigger processor  36  and a defect discarding processor  37 . When a detection signal indicating detection of the item  12  is received from the sensor  22 , the camera trigger processor  36  outputs an image capture trigger signal to the computer  32 . The defect discarding processor  37  receives a discarding instruction signal from the computer  32  when the item  12  inspected by the computer  32  is defective. 
     The camera  31  is connected to the computer  32 . The computer  32  is connected to the input device  34  and the display  35 . When an image capture trigger signal is received from the camera trigger processor  36 , the computer  32  outputs an image capture instruction signal to the camera  31 . When the camera  31  receives the image capture instruction signal, the camera  31  captures an image of the item  12 , which is detected by the sensor  22 , at a central portion of an image capturing area on the conveyor  21 . Further, the computer  32  performs an inspection process using the image data captured by the camera  31  and outputs a discarding instruction signal to the defect discarding processor  37  when determining that the item  12  includes a defect and is defective. The defect discarding processor  37  drives the discarding device  23  based on the discarding instruction signal received from the computer  32  to discard the defective item  12  from the conveyor  21 . 
     The computer  32  of the inspection device  30  will now be described with reference to  FIG. 4 . As shown in  FIG. 4 , the computer  32  includes a computer processing unit (CPU)  41 , a memory  42 , and an auxiliary memory  43 . The auxiliary memory  43  is, for example, formed by a hard disk drive (HDD). 
     The memory  42  stores various types of programs Pr and identifier data CD. The programs Pr include a program for defect position registration illustrated by the flowchart in  FIG. 12  and a program for image segment generation illustrated by the flowchart in  FIG. 13 . The identifier data CD includes model data and a parameter that form the identifier  66 . The CPU  41  reads the identifier data CD to function as the identifier  66 . In a state in which the identifier  66  has not completed learning, that is, when the identifier  66  is in a state of pre-learning or learning, an identification result will not have the desired accuracy rate. Accordingly, a learning unit  64  performs supervised learning on the identifier  66  to adjust the parameter forming the identifier data CD to an optimal value. In this manner, in accordance with the level of learning, the identifier  66  includes an identifier  66  that has completed learning and an identifier  66  that has not completed learning. The identifier  66 , which has completed learning, has the desired accuracy rate. The identifier  66 , which has not completed learning, is in a state of pre-learning or learning and does not have the desired accuracy rate. Even when the identifier  66  becomes the identifier  66  that has completed learning, the identifier  66  that has completed learning does not finish learning and performs additional learning to obtain a higher accuracy rate or adjust the accuracy rate when subtle changes in the form or the like of a defect of the same item  12  lowers the accuracy rate. 
     Thus, in a state in which the identifier  66  has not completed learning, the identifier data CD is formed by model data and an unadjusted parameter. In a state in which the identifier  66  has completed learning, the identifier data CD is formed by model data and the parameter that has been adjusted through supervised learning performed by the identifier  66  that has not completed learning. The CPU  41  reads the identifier data CD including model data and the parameter adjusted through supervised learning to function as the identifier  66  that has completed learning. The programs Pr include a program used by a teaching data generation unit  52 , which will be described later, to generate teaching data provided for learning from the learning unit  64  to the identifier  66  that has not completed learning. 
     The CPU  41  includes a control unit  51 , the teaching data generation unit  52 , a deep-learning unit  53 , and an inspection processing unit  54 . Each of the units  51  to  54  is a functional unit formed by software configured by the programs Pr executed by the CPU  41 . The control unit  51  comprehensively controls the units  52  to  54 . 
     The teaching data generation unit  52  generates teaching data that is provided by the learning unit  64  to the identifier  66  that has not completed learning when performing supervised learning. The teaching data generation unit  52  includes a defect position registration unit  61 , an image segment generation unit  62 , and a label setting unit  63 . The image segment generation unit  62  serves as one example of a segmentation unit, and the label setting unit  63  serves as one example of a label assignment unit. In addition to the teaching data provided by the learning unit  64  to the identifier  66  for supervised learning, the teaching data generation unit  52  also generates teaching data that is provided by an inference unit  65 , which will be described later, to the identifier  66  as test data for verifying whether the identifier  66  can obtain a desirable accuracy rate as a result of the supervised learning. In the present embodiment, the teaching data generation unit  52  corresponds to one example of a labeled image data generation device. 
     The deep-learning unit  53  includes the learning unit  64 , the inference unit  65 , and the identifier  66 . The learning unit  64  generates an identifier  66  that has completed learning by having the identifier  66  that has not completed learning perform supervised learning using teaching data. In the present example, machine learning is deep learning. The learning unit  64  performs supervised learning by providing the teaching data generated by the teaching data generation unit  52  to the identifier  66  so that a parameter such as a weight coefficient is adjusted to an optimal value. 
     The auxiliary memory  43  stores an image data group IG, which is shown in  FIG. 4 . The image data group IG includes pieces of image data Im (hereafter, also simply referred to as “image Im”) that serve as original images when the teaching data generation unit  52  generates teaching data. In the example shown in  FIG. 4 , the image data group IG includes a training data group TrD and a test data group TsD. The image data Im belonging to the training data group TrD serves as the original image data when the teaching data generation unit  52  generates teaching data for supervised learning. The training data group TrD contains OK image Im 1  and NG image Imd (Im 2  and Im 3 ) shown in  FIG. 3 . As shown in  FIG. 3 , the folder F 1  that is named “OK” stores OK image data Im 1 , the folder F 2  that is named “NG 1 ” stores NG 1  image data Im 2 , and the folder F 3  that is named “NG 2 ” stores NG 2  image data Im 3 . In this manner, in the present example, the NG image data Imd is classified and stored in multiple types of the folders F 2  and F 3  named in accordance with the size or the type of defects. 
     Further, the image data Im belonging to the test data group TsD serves as original image data when the teaching data generation unit  52  generates teaching data for the inference unit  65  to test the accuracy rate of the identifier  66 . The test data group TsD includes pieces of image data differing from that of the training data group TrD. In the same manner as the training data group TrD, the test data group TsD includes OK image Im 1   s  and NG images Im 2   s  and Im 3   s  that are classified into the folders F 1  to F 3  named as shown in  FIG. 3 . In the present embodiment, the names of the folders F 1  to F 3  are used as correct labels assigned to data when the teaching data generation unit  52  generates teaching data from the images Im. In  FIG. 4 , the image data group IG for only a single type of the item  12  is shown in the auxiliary memory  43 . When multiple types of the items  12  are inspected, the image data groups IG will be stored according to type. In this case, the identifier data CD and the identifiers  66  will also be arranged in accordance with type. Further, when the same item  12  has multiple portions as inspection subjects, the image data group IG, the identifier data CD, and the identifier  66  will be arranged for each of the inspection subject portions. 
     The elements of the teaching data generation unit  52  will now be described with reference to  FIG. 4 . The defect position registration unit  61  registers the position of a defect to the ones of images Imd including a defect. When the region and the position of a defect is specified and input by a technician using the input device  34 , the defect position registration unit  61  adds a channel other than a color expression channel that expresses the color of the image Imd to the defect image Imd. The other channel is an auxiliary channel added to the color expression channel. The auxiliary channel is, for example, a channel expressing opacity. In the present example, the auxiliary channel is an alpha channel Imα. The defect position registration unit  61  appends the alpha channel Imα to the defect image Imd in addition to the color expression channel. Then, the defect position registration unit  61  registers the region and the position of the defect to the alpha channel Imα. 
     The image segment generation unit  62  divides the image data Im and generates multiple pieces of image segment data DI. A technician operates the input device  34  to instruct the number of segments to the image segment generation unit  62 . The image segment generation unit  62  divides the image data Im by the instructed number of segments M×N to generate M×N pieces of the image segment data DI (hereafter, also referred to as “image segment DI”). 
     The label setting unit  63  assigns labels to the image segments DI. When the image segment data includes the alpha channel, the label setting unit  63  determines whether a second pixel value that indicates a defect is included. If the second pixel value is included, the label setting unit  63  assigns “NG 1 ” or “NG 2 ”, which is the name of the folder (folder name) that stores the original image, to the image segment data as a label. When the image segment data includes the α channel but not the second pixel value indicating a defect, the label setting unit  63  assigns “OK” to the image segment data as a label. When the image segment data does not include the alpha channel, the label setting unit  63  assigns “OK”, which is the name of the folder (folder name) that stores the original image, to the image segment data as a label. After the labels are assigned, the label setting unit  63  removes the alpha channels from the image segments to generate the labeled image segment data DIm (refer to  FIG. 10B ) as teaching data formed by labeled RGB images. 
     The learning unit  64  in the deep-learning unit  53  has the identifier  66  that has not completed learning perform supervised learning using the labeled image data as teaching data. The learning unit  64  in the deep-learning unit  53  has the identifier  66  learn the teaching data to generate the identifier  66  that has completed learning. 
     In the present example, machine learning is deep learning. The learning unit  64  has the identifier  66  learn the teaching data to determine a parameter. 
     The inference unit  65  inputs images of an inspection subject to the identifier  66  and uses the identifier  66  that has completed learning to inspect for defects based on an output result obtained through deep learning. The inspection through deep learning also determines the type of a defect when a defect is detected. Further, the inference unit  65  is used not only for the actual inspection but also for verifying whether the learning result is appropriate. The inference unit  65  inputs the labeled image segment data DIm, which is generated by the teaching data generation unit  52  using the image data Im in the test data group TsD as original images, to the identifier  66  that has completed learning and compares the output results with the correct labels to test the accuracy rate. When the obtained accuracy rate is higher than or equal to a predetermined value, the learning ends. When the obtained accuracy rate is lower than the predetermined value, learning is performed again after changing a condition by changing the number of segments of the image data Im or the like. 
     The inspection processing unit  54  shown in  FIG. 4  inspects the item  12 , which is the inspection subject captured by the camera  31 , using the identifier  66 . The inspection processing unit  54  includes an image acquisition unit  67 , an image segmentation unit  68  serving as one example of an image segment acquisition unit, and a determination unit  69 . The image acquisition unit  67  obtains the image of the item  12  captured by the camera  31 . The image segmentation unit  68  divides the images obtained by the image acquisition unit  67  into the number of segments that is same as that of the labeled image segment data DIm, which was used by the learning unit  64  as the teaching data for supervised learning. The inspection processing unit  54  sends the image segments divided by the image segmentation unit  68  to the deep-learning unit  53 . In the deep-learning unit  53 , the inference unit  65  inputs the image segments DI received from the image segmentation unit  68  of the inspection processing unit  54  to the identifier  66  to infer which one of “OK”, “NG 1 ”, and “NG 2 ” is appropriate based on the output results of the identifier  66 . The inference result of the inference unit  65  is transmitted from the deep-learning unit  53  to the determination unit  69  of the inspection processing unit  54 . In the inspection processing unit  54 , the determination unit  69  evaluates the inspection based on the inference result from the deep-learning unit  53 . The determination unit  69  determines whether the item  12  has a defect based on the inference result of one of “OK”, “NG 1 ”, and “NG 2 ”. In the present example, the determination unit  69  determines that the item  12  is non-defective when the inference result is “OK” and determines that the item  12  is defective when the inference result is “NG 1 ” or “NG 2 ”. 
     When the inspection result indicates that the item  12  is defective based on the determination result of the inspection processing unit  54 , the computer  32  outputs a discarding instruction signal to the defect discarding processor  37 . When the discarding instruction signal is received, the defect discarding processor  37  drives the discarding device  23  to discard the defective item  12  from the conveyor  21 . 
       FIG. 5A  shows one example of the image data Im captured by the camera  31 . The image data Im is the image data Imd of a defective item (also referred to as “NG image data Imd”) including a defect Dg. As shown in  FIG. 5A , the image Im captured by the camera  31  includes the item  12   g , the belt  25   g , and the guide members  27   g . The camera  31  is configured to capture a color image. As shown in  FIG. 5B , the image Im captured by the camera  31  is configured by three channels, namely, a red channel (R-channel), a green channel (G-channel), and a blue channel (B-channel). 
     A technician operates a mouse to open the defective item folder F 2  or F 3  from a list of the folders shown on the display  35  and select the defective item image Imd so that the defective item image Imd of the item  12   g  that includes the defect Dg (also referred to as “NG image Imd”) is shown on the display  35 . For example, the NG image Imd including the defect Dg shown in  FIG. 5A  is displayed. The technician uses a pointing device such as the mouse of the input device  34  or an electronic pen (stylus pen) to specify and input the region of the defect Dg in the NG image Imd. 
       FIG. 6A  shows the defect Dg in the NG image Imd of  FIG. 5A . When the defect Dg is as shown in  FIG. 6A , the technician operates the pointing device such as the mouse or an electric pen that forms the input device  34  to draw a border line EL, which is indicated by the solid line in  FIG. 6B , along the contour of the defect Dg to outline a defect region DA. Alternatively, the technician operates the pointing device to fill in the defect region DA so that position coordinates of the defect region are specified and input. 
     The defect position registration unit  61  includes a channel addition section  61 A and a pixel value assignment section  61 B. When the channel addition section  61 A receives an input that specifies the position coordinates of the defect region DA from the input device  34 , the channel addition section  61 A adds the alpha channel Imα to the NG image data Imd. Then, the pixel value assignment section  61 B assigns a second pixel value P 2  as the pixel value that indicates a defect to a region Da (hereafter, also referred to as “defect region Da”) corresponding to the defect region DA in the alpha channel. The second pixel value P 2  differs from a first pixel value P 1  that is assigned to a defect-free region. The pixel value assignment section  61 B assigns the second pixel value P 2  to every pixel belonging to the defect region Da that is defined in the alpha channel Imα by the position coordinates, which are the same as the position coordinates of the defect region DA specified on the NG image Imd. 
     As shown in  FIG. 7 , after the defect position is registered, the image ImD of a defective item (also referred to as NG image ImD) is formed by a R-channel ImR, a G-channel ImG, a B-channel ImB, and the alpha channel Imα, in which the second pixel values P 2  are assigned to the defect region Da. After the defect position is registered, the NG image ImD is stored in the folder F 2  or F 3 , which is the same folder as the original image. Alternatively, after the defect position is registered, the NG image is stored in the folder F 2  or F 3 , which differs from that of the original image but has the same folder name as that of the original image. 
     The image segment generation unit  62  divides the images Im, which are for learning, stored in the folders F 1  to F 3 . The images Im of the segmentation subject for the image segment generation unit  62  are the OK images Im 1  in the non-defective item folder F 1  and the NG images ImD including the alpha channel Imα in the defective item folders F 2  and F 3 . The image segment generation unit  62  displays an input field for the number of segments on the display  35 . A technician operates the input device  34  to input the number of segments that is appropriate for learning. The number of segments of an image is specified by inputting number M of vertical segmentation and number N of lateral segmentation of an image. Here, M and N are natural numbers, and at least one of M and N is greater than or equal to two. 
     As shown in  FIG. 8 , the image segment generation unit  62  divides the image data Im into M×N. This generates M×N of the image segments DI. As shown in  FIG. 8 , M×N of the image segments DI are classified into K of image segments including at least part of the defect and (M×N−K) of image segments not including the defect. The image segments DI further include ones without any portion of the item  12   g .  FIG. 8  shows an example in which the image Im is divided into 48, or 6×8. 
     In the present embodiment, it is preferred that the image segment generation unit  62  divide the image data Im in a manner so that boundaries between two segments located next to each other are partially overlapped with each other. As shown in  FIG. 9A , when an other image segment is located next to the image segment DIo at both sides in the vertical direction and the lateral direction, the image segment generation unit  62  expands the image segment DIo toward both sides in the vertical direction and the lateral direction by an overlap amount ΔL. 
     Also, when the image segment DI is located along an edge of the image data Im, an other image segment does not exist at the outer side of the image segment DI in at least one of the vertical direction and the lateral direction. With respect to the direction in which an other image segment does not exist next to the image segment DIo, the image segment generation unit  62  expands the image segment DIo toward the side where an other image segment exists by a length two times greater than the overlap amount ΔL. For example, as shown in  FIG. 9B , for the image segment DIo located at the upper left corner of the image data Im, an other image segment exists only in the downward direction and the rightward direction. Accordingly, the image segment generation unit  62  expands the image segment DIo shown in  FIG. 9B  downward and rightward each by a length two times greater than the overlap amount ΔL. In this manner, the image Im is divided into multiple image segments DI having the same size and partially overlapped with the adjacent image segments DI. 
     After every image Im in the folders F 1  to F 3  is divided into M×N of the image segments DI, the label setting unit  63  then assigns correct labels (hereafter, referred to simply as “labels”) to the image segments DI. Specifically, the label setting unit  63  generates a set of data associating the image segment DI with a label. 
     As shown in  FIG. 10A , for the image segment DI obtained from the defective item image ImD, the label setting is performed using the image segment DI to which the alpha channel DIα (hereafter, also denoted by “α-channel DIα”) is added. The image segment generation unit  62  sets the label of the non-defective item image segment DI to “OK” that is the name of the folder F 1  to which the original image of the image segment DI belongs. The image segment DI obtained from the defective item image ImD is formed by a R-channel DIR, a G-channel DIG, a B-channel DIB, and an α-channel DIα. 
     As shown in  FIG. 10A , for the image segment DI, of which the original image belongs to the defective item folder F 2  or F 3 , the image segment generation unit  62  sets the label to the name “NG 1 ” or “NG 2 ” of the folder F 2  or F 3  of the original image when the second pixel value P 2  indicating a defect is included in the α-channel DIα of the image segment DI. For the image segment DI that does not include the second pixel value P 2 , the image segment generation unit  62  sets the label to “OK” regardless of whether the original image belongs to the folder F 2  or F 3 . 
     After a label is set to every image segment DI, the label setting unit  63  extracts the RGB image with the label from the labeled image segment DI as shown in  FIG. 10B . In other words, the label setting unit  63  removes the α-channel DIα from the image segment DI to obtain the labeled image segment data DIm (hereafter, also referred to as “teaching data DIm”) as teaching data. Subsequently, as shown in  FIG. 10C , when the labeled image segment data DIm is received, the learning unit  64  converts the image segment data of the labeled image segment data DIm into array data without the label. In array data, the pixel value of every pixel in the image segment DI are arranged in a predetermined sequence. 
     As shown in  FIG. 11 , the deep-learning unit  53  includes the identifier  66 . When learning is performed, the identifier  66  is in a state prior to completion of learning (includes pre-learning or pre-transfer learning). When the learning unit  64  provides teaching data to the identifier  66  so that a desirable accuracy rate is obtained as a result of the supervised learning, the identifier  66  completes learning. In other words, the learning unit  64  has the identifier  66  that has not completed learning perform supervised learning to generate the identifier  66  that has completed learning. The configuration of the identifier  66  will now be described without particularly distinguishing before learning and after learning. 
     The identifier  66  has a model of a neural network. For example, a convolutional neural network or the like is employed as the model. The identifier  66  includes an input layer  81 , a middle layer (hidden layer)  82 , and an output layer  83 . The input layer  81  includes perceptrons. In the example shown in  FIG. 11 , the number of perceptrons included is “n” (x 1 , . . . xk, . . . xn). For example, when the image segment DI is image data including 50×50 pixels, or a total of 2500 pixels, the array data is formed by an array of 7500 pieces of pixel value data since one pixel has three pixel values, namely, an R-value, a G-value, and a B-value. In the example, the input layer  81  includes 7500 perceptrons (x 1  to xn (n=7500)). The middle layer  82  includes multiple layers formed by perceptrons. The output layer  83  includes the same number of perceptrons as that of the correct labels. The perceptrons in the output layer  83  output the output results. 
     When dividing an image, precision is provided as described below. In the image Im, when the number of pixels of the defect Dg is denoted by TP (true positive) and the number of pixels of the defect-free region is denoted by FP (false positive), precision Prec 1  is expressed by Prec 1 =TP/(TP+FP). When the image Im is divided by M×N and if an entire defect is included in one image segment, precision Prec 2  is expressed by TP/(TP+(FP/(M×N))). That is, the precision Prec 2  can be expressed by Prec 2  (M×N) Prec 1 . Accordingly, when the labeled image segment data Dim, which is obtained by dividing the image Im by (N×M), is used as teaching data, the precision is increased by approximately (M×N) times. Thus, when the image segment data DIm having a high precision is used for supervised learning, the generated identifier  66  has a high accuracy rate. 
     The operation of the inspection device  30  will now be described. First, a defect position registration process that is performed when the computer  32  executes a program will be described with reference to  FIG. 12 . This process is performed by the defect position registration unit  61  of the teaching data generation unit  52 . For example, a technician starts an application software stored in the computer  32  for generation of labeled image data. The technician operates the input device  34  on the application software to specify a folder that contains an original image for generating labeled image data. Specifically, the technician specifies the defective item folder F 2  or F 3  that contains the NG image Imd to display on the computer  32 . The technician may operate the mouse forming the input device  34  to open the folder and individually select the NG image Imd to display on the computer  32 . 
     In step S 11 , the computer  32  reads the NG image from a defective item folder and displays the image. Specifically, the computer  32  reads the first NG image Imd from the specified defective item folder F 2  or F 3  of the folders F 1  to F 3  stored in the auxiliary memory  43  and displays the image on the display  35 . 
     On the NG image Imd shown on the display  35 , the technician outlines the defect Dg with the border line EL along the contour using the pointing device such as a mouse or an electronic pen in accordance with a message shown on the display  35  to specify the defect region DA. 
     In step S 12 , the computer  32  determines whether the defect region DA has been specified. When the technician operates the pointing device to connect the start point and the end point of the border line EL so that the outlined region is defined, the computer  32  determines that the defect region DA has been specified. When the defect region DA has been specified, the computer  32  visually notifies the technician of the specified region, for example, by filling the specified region with a predetermined color, as indicated by the cross-hatching in  FIG. 6B . 
     In step S 13 , the computer  32  adds the alpha channel Imα to the NG image Imd. That is, the computer  32  adds an other channel to the three RGB color expression channels, which form the NG image Imd. In the present embodiment, the other channel is the alpha channel Imα serving as an auxiliary channel that expresses the opacity of an image with a pixel value. In this manner, as shown in  FIG. 7 , the image ImD including the alpha channel is formed by four channels, namely, the R-channel ImR, the G-channel ImG, the B-channel ImB, and the alpha channel Imα. In the present embodiment, the process of step S 13  corresponds to one example of “channel addition step” in which an other channel is appended to an image of the inspection subject in addition to one or more channels forming the image. 
     In step S 14 , the computer  32  registers the defect region DA to the alpha channel Imα. Specifically, the computer  32  adds the second pixel value P 2  to the region Da as the pixel value indicting a defect. The second pixel value P 2  differs from the first pixel value P 1 , which is added to a defect-free region other than the region Da. The region Da corresponds to the defect region DA in the alpha channel Imα. In the example shown in  FIG. 7 , the computer  32  registers the second pixel value P 2 , which is indicated by the black color in  FIG. 7 , to the region Da corresponding to the defect region DA in the α-channel Imα, or the defect region Dα in the α-channel Imα. Then, the computer  32  adds the first pixel value P 1 , which is indicated by the white color in  FIG. 7 , to the defect-free region in the α-channel Imα. For example, the first pixel value P 1  is the initial value of the α-channel Imα and is set to the value of 100% opacity or the like. The second pixel value P 2   a  is set to a value other than 100% opacity. Any pixel value can be set as the first pixel value and the second pixel value as long as the values are different. For example, when the pixel value of the image is 256 gradations, the first pixel value may be “255” and the second pixel value may be “0”, or vice versa. In the present embodiment, the process of step S 14  corresponds to one example of “pixel value assignment step” in which the second pixel value is assigned to the region corresponding to the defect region in the other channel of the image as the pixel value indicating a defect. The second pixel value differs from the first pixel value, which is assigned to the defect-free region in the other channel of the image. 
     In step S 15 , the image ImD including the alpha channel is stored in the memory  43 . Thus, the memory  43  stores the NG image ImD with the alpha channel, in which the second pixel value P 2  is assigned to the region Dα corresponding to the defect region DA in the alpha channel Imα. 
     In step S 16 , the computer  32  determines whether every image has been processed. If every image has not been processed, the computer  32  returns to step S 11  and repeats steps S 11  to S 15  on a subsequent NG image Imd. In the present example, when the processes of steps S 11  to S 15  are performed on every NG image Imd in the defective item folders F 2  and F 3 , an affirmative determination is given in step S 16  and the defect position registration process ends. In this manner, the NG images ImD including the alpha channel are kept in the folders F 2  and F 3  in correspondence with the type of defects and stored in the memory  43 . 
     An image segment generation process will now be described with reference to  FIG. 13 . After the defect position registration process illustrated in  FIG. 12  ends, the computer  32  performs an image segment generation routine illustrated in  FIG. 13 . The image segment generation process is performed by the image segment generation unit  62  and the label setting unit  63 . In advance, a technician operates the mouse or the like to specify and input a desirable number of segments M×N in input field of the number of segments on a setting screen shown on the display  35  by the application software. 
     In step S 21 , the computer  32  reads images from the folders F 1  to F 3 . The computer  32  first reads the first image Im. 
     In step S 22 , the computer  32  segments the image Im into the specified number of segments. As shown in  FIG. 8 , the image Im is divided into the specified number of segments M×N of the image segments DI. In this case, as shown in  FIGS. 9A and 9B , the image Im is divided into M×N of the image segments DI having the same size and overlapped with the adjacent image segments DI by the overlap amount ΔL or 2·ΔL. The computer  32  sets a label to each image segment DI in steps S 23  to S 27  as described below. In the present embodiment, the process of step S 22  corresponds to one example of “segmentation step” in which an image including the other channel is segmented into multiple image segments. 
     In step S 23 , the computer  32  determines whether the image segment DI is an OK image. Here, the OK image refers to the image segment DI divided from an OK image stored in the folder F 1  named “OK”. In other words, when the original image of the image segment DI belongs to the folder named “OK”, the image segment DI is an OK image. When the image segment DI is not an OK image, the computer  32  proceeds to step S 24 . When the image segment DI is an OK image, the computer  32  proceeds to step S 26 . 
     In step S 26 , the computer  32  sets the label of the image segment DI to the name of the folder to which the original image belongs. For example, for the image segment DI determined as an OK image in step S 23 , the computer  32  sets the label of “OK”, which is the name of the folder F 1  to which the original image belongs. 
     In step S 24 , the computer  32  determines whether the image segment DI includes the α-channel That is, the computer  32  determines whether the image segment DI is divided from an NG image ImD with the α-channel and a technician has performed the defect position registration process so that the image segment DI includes the α-channel DIα. When the image segment DI includes the α-channel, the computer  32  proceeds to step S 25 . When the image segment DI does not include the α-channel, the computer  32  proceeds to step S 26 . For example, the image segment DI does not include the α-channel when the original image of the image segment DI belongs to the defective item folder F 2  or F 3  but the defect position has not been registered to the image segment DI. Even if an image of which the defect region DA has not been specified by the technician is mixed, the computer  32  sets the label of the image segment DI of the image to “NG 1 ” or “NG 2 ”, which is the name of the folder to which the original image belongs. 
     In step S 25 , the computer  32  determines whether the image segment DI includes the defect region DA. When a pixel of the second pixel value P 2  indicating a defect is included in the α-channel DIα of the image segment DI, the computer  32  determines that the defect region DA is included. When a pixel of the second pixel value P 2  is not included in the α-channel DIα of the image segment DI, the computer  32  determines that the defect region DA is not included. When the defect region DA is included in the image segment DI, the computer  32  proceeds to step S 26 . When the defect region DA is not included in the image segment DI, the computer  32  proceeds to step S 27 . 
     In step S 26 , when the defect region DA is included in the image segment DI, the computer  32  sets the label of the image segment DI to “NG 1 ” or “NG 2 ”, which is the name of the defective item folder F 2  or F 3  to which the original image of the NG image ImD belongs. 
     In step S 27 , the computer  32  sets the label of the image segment DI to “OK”. That is, even when the image segment DI is not an OK image (or NG image) and includes the α-channel, if the defect region DA is not included in the image segment DI, the computer  32  sets the label of the image segment DI to “OK”. In the present embodiment, the processes of step S 25  (affirmative determination) and step S 26  correspond to a process for assigning a defect label to the image segment DI, in which the second pixel value P 2  indicating a defect is included in the other channel, among multiple image segments DI. Further, the processes of step S 25  (negative determination) and step S 27  correspond to a process for assigning a non-defect label to the image segment DI, in which the second pixel value P 2  indicating a defect is not included in the other channel Therefore, the processes of steps S 25  to S 27  corresponds to one example of “label assignment step”. 
     In step S 28 , the computer  32  stores the image segment DI in the memory  43  as the RGB image. In this case, when the image segment DI includes the α-channel, the computer  32  removes the α-channel DIα from the labeled image segment DI and stores the labeled image segment data DIm formed by the labeled RGB image in the memory  43  as teaching data. Further, when the image segment DI does not include the α-channel, the computer  32  directly stores the image segment DI formed by the labeled RGB image in the memory  43  as the labeled image segment data DIm. In the present embodiment, the process of step S 28  corresponds to one example of “channel removal step” in which the other channel is removed from the image segment DI. 
     In step S 29 , the computer  32  determines whether every image segment DI has been processed. When there is an unprocessed image segment DI, the computer  32  returns to step S 23  and performs steps S 23  to S 28  on a subsequent image segment DI to set the label of the subsequent image segment DI. Then, after steps S 23  to S 28  are performed on every image segment DI and the label is set to every image segment DI (affirmative determination in S 29 ), the computer  32  proceeds to step S 30 . 
     In step S 30 , the computer  32  determines whether every image Im has been processed. When there is an unprocessed image Im, the computer  32  returns to step S 21  and performs steps S 21  to S 29  on a subsequent image Im to set the label to every image segment DI divided from the subsequent image Im. Then, after steps S 21  to S 29  are performed on every image Im so that every image Im is divided and the label is set to every image segment DI (affirmative determination in S 30 ), the computer  32  ends the image segment generation routine. In this manner, the labeled image segment data DIm is generated as teaching data and stored in the memory  43 . For example, when a total of one-hundred pieces of the image data Im are in the folders F 1  to F 3  and the number of segments is M×N=48, each image Im is divided into 48 segments and 4800 pieces of the labeled image segment data DIm are generated as teaching data. 
     Next, the learning unit  64  performs supervised learning using the labeled image segment data DIm as teaching data. As shown in  FIG. 10C , the learning unit  64  converts the RGB images of the labeled image segment data Dim, excluding the labels, into array data that is formed by arrangement of pixel values. Subsequently, as shown in  FIG. 11 , the learning unit  64  normalizes the array data and inputs the normalized array data into the input layer  81  of the identifier  66  that has not completed learning. The normalized array data is input to each of the perceptrons x 1  to xn in the input layer  81 . 
     For example, the normalized array data of 4800 RGB images of the teaching data DIm is sequentially input to the input layer  81  of the identifier  66 . The identifier  66  performs a learning process based on the normalized array data input to the input layer  81  through the neural network, which includes the input layer  81 , two to one-hundred and fifty middle layers  82 , and the output layer  83 . In the learning process, a backpropagation or the like is performed to adjust the weight of each layer. The difference between the output value and the correct label is backpropagated to each layer so that the output value becomes closer to the correct value. In this manner, optimal parameters such as weight W 1  and weight W 2  are determined. 
     The teaching data DIm, in which labels are assigned to the image segments DI divided from the image Im, is used so that a high accuracy rate can be expected from the identifier  66 . Further, if a desirable accuracy rate is not obtained, a technician changes the number of segments and regenerates the teaching data DIm. In this case, the NG image ImD still includes the alpha channel Imα, which was added at the beginning, and the second pixel value P 2 , which was assigned to the defect region DA. In this manner, even when the image segment generation unit  62  re-divides the image Im into the number of segments that is respecified by the technician, the label setting unit  63  can set an appropriate label to the image segment DI in accordance with whether the second pixel value P 2  is included in the alpha channel DIα of the re-divided image segment DI. Therefore, the technician may specify the defect region DA only once on the image Imd, and this type of task will not be needed for re-segmentation of the image Imd. This facilitates the process of setting a label to each image segment DI divided from the image Im and generating the teaching data Dim, which, in turn, reduces the cost of learning. In the described example, a few NG images Imd are included in one-hundred images Im. However, a massive number of the images Im are actually needed for the learning of the identifier  66  in order to obtain a desirable accuracy rate. Therefore, the specification of the defect region DA on the NG image Imd through a single task reduces the burden of generating the teaching data Dim. 
     A method for inspection includes an identifier generation step, an image segment acquisition step, and a determination step. In the identifier generation step, the learning unit  64  performs supervised machine learning using the labeled image segment data DIm as teaching data to generate the identifier  66 . In the image segment acquisition step, the image segmentation unit  68  obtains the image segments DI by dividing the image Im of the item  12  into the number of segments that is same as that of the image segment DI, which is used for generating the teaching data DIm to generate the identifier  66 . In the determination step, the determination unit  69  inputs the image segments DI to the identifier  66  to determine whether the item  12  includes a defect based on the output value output from the identifier  66  as the identification result. The determination unit  69  determines whether the item  12  is a non-defective item or a defective item. In particular, when the item  12  is determined to be a defective item, the computer  32  instructs the defect discarding processor  37  to discard the item  12  from the conveyor  21  with the discarding device  23 . Also, when the item  12  is determined to be a defective item by the inspection, the display  35  shows that the item  12  is defective and the type of the defect based on the identification result of the identifier. 
     As described above, the present embodiment has the following advantages. 
     (1) With the method for generating labeled image data in accordance with the present embodiment, the labeled image segment data DIm is generated as teaching data, which is used for having the identifier  66  that has not completed learning perform supervised machine learning so that the identifier  66  is generated for the inspection that determines whether the defect Dg is included in the captured image Im of the inspection subject. The method for generating labeled image data includes the channel addition step (step S 13 ), the pixel value assignment step (step S 14 ), the segmentation step (step S 22 ), the label assignment step (steps S 25  to S 27 ), and the channel removal step (step S 28 ). In the channel addition step (S 13 ), the alpha channel Imα is appended to the image Im of the inspection subject as an other channel in addition to the color expression channels ImR, ImG, and ImB that form the image Im. In the pixel value assignment step (S 14 ), the second pixel value P 2  differing from the first pixel value P 1 , which is assigned to a defect-free region, is assigned as the pixel value indicting a defect to the region Dα corresponding to the defect region DA in the alpha channel Imα of the image Im. In the segmentation step (S 22 ), the image ImD including the alpha channel Imα is segmented into multiple image segments DI. In the label assignment step (S 25  to S 27 ), among the image segments DI, a defect label (“NG 1 ” or “NG 2 ” label) is assigned to the image segment DI including the second pixel value P 2  in the alpha channel DIα, and a non-defect label (“OK” label) is assigned to the image segment DI not including the second pixel value P 2  in the alpha channel DIα. In the channel removal step (S 28 ), the alpha channel DIα is removed from the image segments DI to generate the labeled image segment data DIm. This facilitates the process of generating the labeled image segment data DIm and reduces the learning cost even when the labeled image segment data DIm serving as teaching data is generated by dividing the captured image Im. For example, even when the number of segments of the image Im is changed for regeneration of the labeled image segment data DIm, a technician does not need to respecify the defect region DA in the image Im whenever the number of segments is changed. This facilitates the process and reduces the learning cost. Further, the image Im is divided to generate the labeled image segment data DIm. This improves the precision compared to when the image Im is not divided. Accordingly, the labeled image segment data DIm with a high precision is used for supervised learning so that the identifier  66  can be generated with a high accuracy rate. Therefore, the deep learning performed using the identifier  66  with a high accuracy rate allows the item  12  to be inspected for defects at a high degree of accuracy. 
     (2) In the segmentation step (S 22 ), the image ImD including the alpha channel Imα is segmented into multiple image segments DI, in which the adjacent image segments DI are partially overlapped with each other. Accordingly, even when the defect Dg is shown over multiple image segments DI, the defect Dg is split less frequently. Thus, a high accuracy rate is readily obtained when learning is performed using the labeled image segment data Dim. 
     (3) The alpha channel Imα is used as the other channel. The alpha channel Imα that sets the opacity of an image is used. This allows the second pixel value P 2  indicating a defect to be assigned to the region Da, which corresponds to the defect region DA, relatively easily without affecting the color expression channels of the image Im captured by the camera  31 . 
     (4) When an accuracy rate greater than or equal to a predetermined value is not obtained from the output as a result of the input of test data to the identifier  66 , which is generated by performing supervised machine learning using the labeled image segment data DIm as teaching data, the number of segments used in the segmentation step (S 22 ) is changed. In this manner, when an accuracy rate greater than or equal to a predetermined value is not obtained and the number of segments used in the segmentation step (S 22 ) is changed to regenerate the labeled image segment data DIm, the image already includes the alpha channel Imα and the second pixel value P 2  indicating a defect. Thus, when the number of segments of the image Im is changed to regenerate the labeled image segment data DIm, there is no need for a technician to operate the input device  34  to specify the defect region DA so that the region Dα is specified and the second pixel value P 2  is assigned in the alpha channel Imα. 
     (5) The method for inspection includes the identifier generation step, the image segment acquisition step, and the determination step. In the identifier generation step, supervised machine learning is performed using the labeled image segment data DIm as teaching data to generate the identifier  66 . In the image segment acquisition step, multiple image segments DI are obtained by dividing the image Im of the inspection subject into the number of segments that is same as that of the image segment DI, which is used for generating the teaching data to generate the identifier  66 . In the determination step, the image segments DI are input to the identifier  66  to determine whether the item  12  is defective or non-defective based on the output value output from the identifier  66  as the identification result. This facilitates generation of the labeled image segment data DIm provided as teaching data when having the identifier  66  that has not completed learning perform supervised learning. Thus, the item  12  can be inspected for defects at a lower learning cost. 
     (6) The machine learning is deep learning. Deep learning allows the item  12  to be accurately inspected for defects at a lower learning cost. 
     (7) The computer  32  executes the programs Pr to generate the labeled image segment data DIm as teaching data, which is used for having the identifier  66  that has not completed learning perform supervised machine learning, so that the identifier  66  is generated for the inspection that determines whether a defect is included in the captured image Im of the item  12 . The computer  32  sequentially executes the channel addition step (S 13 ), the pixel value assignment step (S 14 ), the segmentation step (S 22 ), the label assignment step (S 25  to S 27 ), and the channel removal step (S 28 ) included in the programs Pr. In the channel addition step (S 13 ), the computer  32  appends the alpha channel Imα to the image Im of the inspection subject as an other channel in addition to the color expression channels that form the image Im. In the pixel value assignment step (S 14 ), the computer  32  assigns the second pixel value differing from the first pixel value P 1 , which is assigned to a defect-free region, as the pixel value indicting a defect to the region Dα corresponding to the defect region DA in the alpha channel Imα of the image. In the segmentation step (S 22 ), the computer  32  segments the image ImD including the alpha channel Imα into multiple image segments DI. In the label assignment step (S 25  to S 27 ), among the image segments DI, the computer  32  assigns a defect label (“NG 1 ” or “NG 2 ” label) to the image segment DI including the second pixel value P 2  in the alpha channel Imα and assigns a non-defect label to the image segment DI not including the second pixel value P 2  in the alpha channel Imα. In the channel removal step (S 28 ), the computer  32  removes the alpha channel Imα from the image segments DI to generate the labeled image segment data DIm. 
     The computer  32  executes the programs Pr so that even when the labeled image segment data DIm used as teaching data for supervised machine learning is generated by dividing the captured image Im, the process is facilitated and the learning cost is reduced. For example, even when the number of segments of the image Im is changed for regeneration of the labeled image segment data DIm, a technician does not need to respecify the defect region DA in the image Im whenever the number of segments is changed. This facilitates the process and reduces the learning cost. 
     (8) The teaching data generation unit  52  serving as one example of a labeled image data generation device generates the labeled image segment data DIm as teaching data, which is used for having the identifier  66  that has not completed learning perform supervised machine learning so that the identifier  66  is generated for the inspection that determines whether a defect is included in a captured image of the inspection subject. The teaching data generation unit  52  includes the channel addition section  61 A, the pixel value assignment section  61 B, the image segment generation unit  62 , and the label setting unit  63 . The channel addition section  61 A appends the alpha channel Imα to the image Im of the inspection subject as an other channel in addition to one or more channels that form the image Im. The pixel value assignment section  61 B assigns the second pixel value P 2  differing from the first pixel value P 1 , which is assigned to a defect-free region, as the pixel value indicting a defect to the region Dα corresponding to the defect region DA in the alpha channel Imα of the image Im. The image segment generation unit  62  divides the image Im including the alpha channel Imα into multiple image segments DI. Among the image segments DI, the label setting unit  63  assigns a defect label (“NG 1 ” label or “NG 2 ” label) to the image segment DI that includes the second pixel value P 2  in the alpha channel DIα and assigns a non-defect label (“OK” label) to the image segment DI that does not include the second pixel value P 2  in the alpha channel DIα. Further, after the labels are assigned, the alpha channel DIα is removed from the image segments DI to generate the labeled image segment data DIm. In this manner, the teaching data generation unit  52  can assign an appropriate label to each image segment DI even when the labeled image segment data DIm used as teaching data for supervised learning is generated by dividing the captured image Im as long as the defect region DA is specified once on the original image. For example, if a desirable accuracy rate is not obtained from the identifier  66  as a result of the learning, the number of segments of the image Im is changed to regenerate the labeled image segment data Dim. In this case, the alpha channel Imα is already added to the image Im and the second pixel value P 2  is already assigned to the region Dα corresponding to the defect region DA. Thus, a technician may only change the number of segments and start dividing the image ImD with the computer  32 . That is, the technician does not have to specify the defect region DA on the image Im whenever the number of segments is changed. This facilitates generation of the labeled image segment data DIm and reduces the learning cost. 
     (9) The inspection device  30  inspects the item  12  for defects based on the captured image Im of the item  12 , or the inspection subject. The inspection device  30  includes the teaching data generation unit  52 , the identifier  66 , the image segmentation unit  68 , and the determination unit  69 . The identifier  66  is generated through performance of supervised machine learning using the labeled image segment data DIm as teaching data, which is generated by the teaching data generation unit  52 . The image segmentation unit  68  obtains multiple image segments DI by dividing the image Im of the item  12 , or the inspection subject, into the number of segments that is same as that of the image segment DI, which is used for generating the labeled image segment data DIm to generate the identifier  66 . The identifier  66  receives the image segments DI obtained by the image segmentation unit  68  and outputs an identification result. The determination unit  69  determines whether the item  12  is defective based on the identification result from the identifier  66 . The inspection device  30  facilitates generation of the labeled image segment data DIm used for learning of the identifier  66  that has not completed learning thereby allowing the item  12  to be inspected for defects at a lower learning cost. 
     Second Embodiment 
     A second embodiment will now be described with reference to the drawings. The second embodiment differs from the first embodiment in that information on the type of label is also added to the alpha channel. Thus, the configuration same as the first embodiment will not be described, and only the differences will be described. 
     As shown in  FIG. 14 , the defect position registration unit  61  performs a process partially different from that of the first embodiment. The defect position registration unit  61  shows the contents shown in  FIGS. 14A and 14B  on the display  35 . When an NG image belonging to the defective item folder F 2  named “NG 1 ” is displayed, the display  35  shows input portions  71  and  72  that specify the type of defects. In the example shown in  FIG. 14A , the input portion  71  specifies the type “NG 1 ”, and the input portion  72  specifies the type “NG 2 ”. A technician operates the pointing device such as a mouse or an electronic pen to draw the border line EL along the contour of defect Ds so that the defect region DA is specified. Then, the technician selects the input portion  71  that corresponds to the type of the defect Ds with the mouse or the like. Subsequently, the channel addition section  61 A adds the alpha channel Imα to the NG image Im 2  shown in the right side of  FIG. 14A . Also, the pixel value assignment section  61 B assigns a second pixel value P 21  corresponding to the type of the defect selected with the input portion  71  to the region Dα 1 , which corresponds to the defect region DA in the added alpha channel Imα of the image Im. 
     In the example shown in  FIG. 14B , a technician operates the pointing device such as a mouse or an electronic pen to draw the border line EL along the contour of defect D 1  so that the defect region DA is specified and input. Then, the technician selects the input portion  72  that corresponds to the type of the defect D 1  with the mouse or the like. Then, the defect position registration unit  61  adds the alpha channel Imα to the NG image Im 3  shown in the right side of  FIG. 14B  and assigns a second pixel value P 22  corresponding to the type of the defect, which is selected with the input portion  72 , to the region Dα 2  corresponding to the defect region DA in the image Im in the added alpha channel Imα. In the examples shown in  FIG. 14 , the type of a defect is classified in accordance with the size of the defect. Alternatively, the type of a defect may be classified in accordance with other characteristics. 
     The operation of the inspection device  30  in accordance with the second embodiment will now be described with reference to  FIG. 15 . The defect position registration unit  61  performs the process shown in  FIG. 14 . Accordingly, the second pixel value P 21  or P 22  corresponding to the type of the defect is set to the region Dα 1  or Dα 2  corresponding to the defect region DA in the alpha channel Imα. The computer  32  performs an image segment generation routine illustrated in  FIG. 15  to function as the image segment generation unit  62 . The image segment generation process performed by the computer  32  will now be described. 
     In  FIG. 15 , steps S 23  to S 27  are the same as those in the first embodiment and step S 31  differs from the first embodiment. Further, steps S 21 , S 22 , and S 28  to S 30  illustrated in  FIG. 13  are the same as those in the first embodiment and thus omitted in  FIG. 15 . The computer  32  displays the first image read from one of the folders F 1  to F 3  (S 21 ). In advance, a technician inputs the desirable number of segments M×N in the input field for the number of segments on the setting screen shown on the display  35 . The computer  32  segments the image into M×N image segments (S 22 ). 
     In step S 23  shown in  FIG. 15 , the computer  32  determines whether the image segment DI is an OK image. When the image segment DI is not an OK image, the computer  32  proceeds to step S 24 . When the image segment DI is an OK image, the computer  32  proceeds to step S 26 . In step S 26 , the computer  32  sets the label of the image segment DI to “OK”, which is the name of the folder to which the original image belongs. 
     In step S 24 , the computer  32  determines whether the image segment DI includes the α-channel. When the image segment DI includes the α-channel, the computer  32  proceeds to step S 25 . When the image segment DI does not include the α-channel, the computer  32  proceeds to step S 26 . The name of the folder “NG 1 ” or “NG 2 ” of the original image is set as the label of the image segment DI, which is not an OK image and does not include the α-channel. 
     In step S 25 , the computer  32  determines whether the image segment DI includes the defect region DA. When a pixel of the second pixel value P 2  indicating a defect is included in the α-channel DIα of the image segment DI, the computer  32  determines that the defect region DA is included. When a pixel of the second pixel value P 2  indicating a defect is not included in the α-channel DIα of the image segment DI, the computer  32  determines that the defect region DA is not included. When the defect region DA is included in the image segment DI, the computer  32  proceeds to step S 31 . When the defect region DA is not included in the image segment DI, the computer  32  proceeds to step S 27 . 
     In step S 31 , the computer  32  sets a label corresponding to the second pixel value P 2  in the defect region Dα in the alpha channel DIα. Specifically, when the second pixel value P 2  in the defect region Dα in the alpha channel Imα is the second pixel value P 21  indicating a first defect Ds, the computer  32  sets a label “NG 1 ” in correspondence with the second pixel value P 21 . Further, when the second pixel value P 2  in the defect region Dα in the alpha channel lima is the second pixel value P 21  indicating a second defect D 1 , the computer  32  sets a label “NG 2 ” in correspondence with the second pixel value P 22 . 
     In step S 27 , the computer  32  sets the label of the image segment DI to “OK”. That is, even when the image segment DI is not an OK image (or NG image) and includes the α-channel, if the defect region DA is not included in the image segment DI, the computer  32  sets the label of the image segment DI to “OK”. 
     When multiple types of defects are included in the image segments DI divided from the image Im, appropriate labels can be set to the image segments DI in correspondence with the types of the defects. Thus, a high accuracy rate can be expected to be obtained from the identifier  66  that is generated after performing the learning. Further, even if a technician changes the number of segments when a desirable accuracy rate is not obtained, the re-divided image segments DI still include the initially-added α-channel Imα and the second pixel value P 21  or P 22  assigned to the defect region Dα 1  or Dα 2 . Thus, labels can automatically be set to the re-divided image segments DI in accordance with the second pixel values P 21  and P 22  and whether the second pixel values P 21  and P 22  are included in the α-channel DIα. In this manner, the technician may specify and input the defect region DA through merely a single task on the image Im and this task will not be required for re-segmentation of the image Im. This facilitates the process of setting labels to each image segments DI divided from the image Im and generating the labeled image segment data DIm. This further reduces the cost of learning. 
     The second embodiment has the following advantage in addition to the advantages of the first embodiment. 
     (10) There are multiple types of the defect D. In the pixel value assignment step (S 14 ), when the second pixel value P 2  is assigned to the alpha channel Imα, the second pixel value P 21  and P 22 , which are different, are added in accordance with the type the defect D. In the label assignment step (S 25  to S 27 ), a different defect label (“NG 1 ” label or “NG 2 ” label) is assigned to the image segment DI including the second pixel value P 2  in accordance with the second pixel values P 21  and P 22 . In this manner, the labeled image segment data DIm is generated including the defect label that differs in accordance with the type of the defect D. 
     The embodiments are not limited to the above description and may be modified as follows. 
     The alpha channel that is an auxiliary channel serving as one example of the other channel does not need to have 256 gradations and may have 64, 32, 16, 8, 4, or 2 gradations. For example, the second pixel value used for indicating the type of a defect may be set to a value that combines information other than the type of a defect. 
     The size and the type of the defect Dg may be combined to assign different second pixel values to the defects belonging to the same size range if the defects are of different types. With this configuration, when the image includes a defect, the defect can be identified by the combination of the size and the type of the defect. 
     The image is not limited to a color image like the RGB image and may be a grayscale image. That is, the image captured by the camera  31  may have a single channel. In this case, the defect position registration unit  61  adds the alpha channel to the grayscale image as the other channel and assigns the second pixel value differing from the first pixel value, which is assigned to a defect-free region, to the region corresponding to the defect region in the added alpha channel. 
     The present disclosure may be applied to semi-supervised learning in which images with correct labels and images without correct labels are mixed. 
     The item  12  of the inspection may be any item having any shape and size. The item  12  may be, for example, a container, such as a plastic bottle and a bin, a food product, a beverage, an electronic component, an electronic product, an everyday item, a part, a member, and a raw material such as powders and a liquid. The item  12  may only be an item that can be inspected for defects from the external appearance. Further, the inspection of the item  12  performed by the inspection device  30  is not limited to the inspection of the external appearance of the item  12 . For example, the inside of the item  12  may be inspected in a non-destructive manner using an image captured by emitting ultrasound waves or X-ray. 
     The inspection subject is not limited to the item  12  and may be a photograph of a building, an aerial photograph of topography, a photograph of the sky, an astrophotograph, a micrograph, or the like. The labeled image segment data may be generated by dividing these photographs. 
     The labeled image data generation device may be configured as a device separate from the inspection device  30 . 
     The supervised or semi-supervised machine learning is not limited to deep learning. For example, the present application may be applied to machine learning performed by a support vector machine (SVM) or a neural network having a single middle layer. 
     The computer  32  may include a graphics processing unit (GPU). In this case, it is preferred that the deep-learning unit  53  including the identifier  66  is mounted on the GPU. In this case, the identifier data CD is stored in a GPU memory. Also, the devices may be configured separately including the inspection functionality and the learning functionality for having the identifier  66  that has not completed learning perform learning. Specifically, the labeled image data generation device that includes the learning functionality for having the identifier  66  that has not completed learning perform learning may be a device separate from the inspection device that inspects an item using the identifier  66 , which has completed learning with the labeled image data generation device. In this case, the labeled image data generation device includes the GPU, and the GPU includes the deep-learning unit  53 . Preferably, the inspection device includes a labeled image data generation device for performing additional learning. In this case, since the processing load when performing additional learning is small, the inspection device does not have to include the GPU, and the CPU may include the deep-learning unit  53 . 
     DESCRIPTION OF THE REFERENCE NUMERALS 
       11 ) inspection system;  12 ) item as one example of inspection subject;  20 ) transfer device;  21 ) conveyor;  22 ) sensor;  23 ) discarding device;  30 ) inspection device;  31 ) camera;  32 ) computer;  33 ) PLC;  34 ) input device;  35 ) display;  36 ) camera trigger processor;  37 ) defect discarding processor;  41 ) CPU;  42 ) memory;  43 ) auxiliary memory;  51 ) control unit;  52 ) teaching data generation unit as one example of labeled image data generation device;  53 ) deep-learning unit;  54 ) inspection processing unit;  61 ) defect position registration unit;  61 A) channel addition section;  61 B) pixel value assignment section;  62 ) image segment generation unit as one example of segmentation unit;  63 ) label setting unit as one example of label assignment unit;  64 ) learning unit;  65 ) inference unit;  66 ) identifier;  67 ) image acquisition unit;  68 ) image segmentation unit as one example of image segment acquisition unit;  69 ) determination unit;  81 ) input layer;  82 ) middle layer;  83 ) output layer; X) transfer direction; C) controller; Pr) program; IG) image data group; TrD) training data group; Im) image data (image); Imd) defective item image (NG image); Imα) alpha channel as one example of an other channel; ImD) defective item image (NG image); D, Ds, Dl) defect; Dg) defect; DA) defect region; Dα, Dα 1 , Dα 2 ) region (defect region); P 1 ) first pixel value; P 2 ) second pixel value; P 21 ) second pixel value; P 22 ) second pixel value; DI) image segment (image segment data); DIα) alpha channel; ΔL) overlap amount; TsD) test data group; DIm) labeled image data as teaching data and one example of labeled image data and test data.