WO2021192627A1 - Système d'inspection, dispositif d'apprentissage, programme d'apprentissage, procédé d'apprentissage, dispositif d'inspection, programme d'inspection et procédé d'inspection - Google Patents

Système d'inspection, dispositif d'apprentissage, programme d'apprentissage, procédé d'apprentissage, dispositif d'inspection, programme d'inspection et procédé d'inspection Download PDF

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WO2021192627A1
WO2021192627A1 PCT/JP2021/003830 JP2021003830W WO2021192627A1 WO 2021192627 A1 WO2021192627 A1 WO 2021192627A1 JP 2021003830 W JP2021003830 W JP 2021003830W WO 2021192627 A1 WO2021192627 A1 WO 2021192627A1
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image
learning
inspection
detection target
learning data
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PCT/JP2021/003830
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English (en)
Japanese (ja)
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学 梅田
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株式会社Ｌｉｘｉｌ
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Priority claimed from JP2020051136A external-priority patent/JP7449739B2/ja
Priority claimed from JP2020051135A external-priority patent/JP2021148719A/ja
Application filed by 株式会社Ｌｉｘｉｌ filed Critical 株式会社Ｌｉｘｉｌ
Publication of WO2021192627A1 publication Critical patent/WO2021192627A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Definitions

  • the present disclosure relates to a technique for inspecting an inspection target, and in particular, an inspection system for inspecting an inspection target, a learning device available for the inspection system, a learning program, a learning method, and an inspection device available for the inspection system. Regarding inspection programs and inspection methods.
  • Patent Document 1 In order to automatically detect such a defect, a technique for determining the surface state from the captured image of the inspection object has been proposed (see, for example, Patent Document 1).
  • the present disclosure is made in view of such a problem, and the purpose thereof is to improve the accuracy of inspecting the inspection target.
  • the inspection system of a certain aspect of the present disclosure uses a learning device that learns a detector for detecting a detection target site to be inspected, and a detector learned by the learning device. It is provided with an inspection device for detecting a detection target site to be inspected.
  • the learning device acquires learning data from a learning image acquisition unit that acquires an image of the inspection target including the detection target part and an image of the detection target part included in the image of the inspection target acquired by the learning image acquisition unit. It includes a learning data generation unit to be generated, and a learning unit to learn a detector using the learning data generated by the learning data generation unit.
  • the learning data generation unit divides at least a part of the images of the plurality of detection target parts into a plurality of images, generates learning data from each of the divided images of the detection target parts, and the inspection device uses the captured image of the inspection target. It is provided with an inspection image acquisition unit for acquiring the data, and a detection unit for detecting a detection target portion from the captured image acquired by the inspection image acquisition unit using a detector.
  • Another aspect of the present disclosure is a learning device.
  • This device acquires learning data from the image of the detection target part included in the image of the inspection target acquired by the learning image acquisition unit and the learning image acquisition unit that acquires the captured image of the inspection target including the detection target part. It includes a learning data generation unit to be generated, and a learning unit to learn a detector for detecting a detection target site to be inspected by using the learning data generated by the learning data generation unit.
  • the learning data generation unit divides at least a part of the images of the plurality of detection target parts into a plurality of images, and generates learning data from each of the divided images of the detection target parts.
  • Yet another aspect of the disclosure is a learning program.
  • This program uses a computer from a learning image acquisition unit that acquires an image of an inspection target including a detection target part and an image of the detection target part included in the image of the inspection target acquired by the learning image acquisition unit. It functions as a learning data generation unit that generates training data and a learning unit that learns a detector for detecting a detection target part to be inspected by using the learning data generated by the training data generation unit.
  • the learning data generation unit divides at least a part of the images of the plurality of detection target parts into a plurality of images, and generates learning data from each of the divided images of the detection target parts.
  • Yet another aspect of the disclosure is a learning method.
  • This method is generated by a computer with a step of acquiring an image of the inspection target including the detection target part and a step of generating learning data from the image of the detection target part included in the acquired image of the inspection target.
  • the step of learning the detector for detecting the detection target part to be inspected and the step of learning are executed.
  • the learning step at least a part of the images of the plurality of detection target parts is divided into a plurality of images, and learning data is generated from each of the divided images of the detection target parts.
  • Yet another aspect of the present disclosure is an inspection device.
  • This device was trained using an inspection image acquisition unit that acquires an image to be inspected and learning data generated by dividing an image of a detection target part included in the image to be inspected into a plurality of images. It includes a detection unit that detects a detection target site from an captured image acquired by an inspection image acquisition unit using a detector.
  • Yet another aspect of the disclosure is an inspection program.
  • This program uses a computer with an inspection image acquisition unit that acquires an image to be inspected, and learning data generated by dividing an image of a detection target part included in the image to be inspected into a plurality of images. Using the learned detector, it functions as a detection unit that detects a detection target site from the captured image acquired by the inspection image acquisition unit.
  • Yet another aspect of the present disclosure is an inspection method.
  • the detection is learned by using the step of acquiring the captured image of the inspection target and the learning data generated by dividing the image of the detection target part included in the captured image of the inspection target into a plurality of images on a computer.
  • the step of detecting the detection target site from the acquired captured image is executed.
  • a learning device that learns a detector for detecting a detection target site to be inspected and a detector learned by the learning device are used to detect the detection target site to be inspected. It is equipped with an inspection device for detecting.
  • the learning device converts the image to be captured before generating learning data from the image acquisition unit for learning that acquires the captured image of the inspection target including the detection target site and the captured image of the inspection target acquired by the learning image acquisition unit.
  • An image processing unit that performs image processing, a learning data generation unit that generates learning data from the image of the detection target part processed by the image processing unit, and a detector using the training data generated by the learning data generation unit. It has a learning department for learning.
  • the inspection device includes an inspection image acquisition unit that acquires a captured image of an inspection target, and a detection unit that detects a detection target portion from the captured image acquired by the inspection image acquisition unit using a detector.
  • Yet another aspect of the present disclosure is a learning device.
  • This device converts the image to be captured before generating learning data from the image acquisition unit for learning that acquires the captured image of the inspection target including the detection target site and the captured image of the inspection target acquired by the image acquisition unit for learning.
  • An inspection target using an image processing unit that performs image processing, a learning data generation unit that generates training data from an image of a detection target part processed by the image processing unit, and training data generated by the training data generation unit. It is provided with a learning unit for learning a detector for detecting a detection target portion of the above.
  • Yet another aspect of the disclosure is a learning program.
  • This program uses the computer to generate training data from the image acquisition unit for learning that acquires the captured image of the inspection target including the detection target site and the captured image of the inspection target acquired by the image acquisition unit for learning.
  • a learning data generation unit that generates learning data from the image of the detection target part processed by the image processing unit, and learning data generated by the training data generation unit.
  • It functions as a learning unit that learns a detector for detecting a detection target part to be inspected.
  • Yet another aspect of the disclosure is a learning method.
  • This method includes a step of acquiring a captured image of an inspection target including a detection target site on a computer, and a step of performing image processing on the captured image before generating learning data from the acquired captured image of the inspection target.
  • a step of generating training data from the processed image of the detection target part and a step of learning a detector for detecting the detection target part of the inspection target using the generated training data are executed.
  • Yet another aspect of the present disclosure is an inspection device.
  • This device performs image processing on the captured image before detecting the detection target portion from the inspection image acquisition unit that acquires the captured image of the inspection target and the captured image of the inspection target acquired by the inspection image acquisition unit. Detected from the captured image acquired by the inspection image acquisition unit using the image processing unit and the detector learned using the learning data generated from the image of the detection target part included in the captured image to be inspected. It is provided with a detection unit for detecting a target site.
  • Yet another aspect of the disclosure is an inspection program.
  • This program displays an image on the captured image before detecting the detection target part from the inspection image acquisition unit that acquires the captured image of the inspection target and the captured image of the inspection target acquired by the inspection image acquisition unit.
  • Yet another aspect of the present disclosure is an inspection method.
  • This method includes a step of acquiring an image to be inspected by a computer, a step of performing image processing on the image to be detected before detecting a part to be detected from the acquired image of the object to be inspected, and an image of the image to be inspected.
  • the step of detecting the detection target site from the acquired captured image is executed.
  • the learning device learns a defect detector for detecting defects such as cracks generated on the surface of the tank.
  • the inspection device detects defects from the captured image of the tank by using the defect detector learned by the learning device.
  • the efficiency and accuracy of the inspection can be improved, so that the burden on the inspector can be significantly reduced.
  • the ratio of determining the accepted product as the rejected product can be reduced, the time and effort for reviewing the rejected product can be reduced.
  • accurate inspections can be carried out regardless of the inspector's experience and skill, it is possible to eliminate the personalization of inspections, reduce the shortage of inspectors, and improve the quality of products. It can be stabilized well.
  • FIG. 1 shows the configuration of the inspection system according to the embodiment.
  • the inspection system 1 includes a tank 2 to be inspected, an imaging device 3 for imaging the appearance of the tank 2, and a learning device 100 for learning a defect detector for detecting defects generated on the surface of the tank 2. It is provided with an inspection device 200 for inspecting the surface of the tank 2 using a defect detector, and an Internet 4 which is an example of a communication network connecting these devices.
  • FIG. 2 shows an example of a captured image of the surface of the tank 2.
  • FIG. 2A shows an image of a crack formed on the surface of the tank 2.
  • the captured image 10 includes images of four cracks 11a, 11b, 11c, and 11d.
  • the learning device 100 attaches an annotation (teacher label) indicating that the crack is a crack to the crack included in the captured image 10, and learns the defect detector by supervised learning.
  • one defect image is divided into a plurality of parts, and learning data is generated from each of the divided defect images. As a result, a large amount of training data can be efficiently generated to learn the defect detector, so that the generalization performance and accuracy of the defect detector can be improved.
  • the learning device 100 may add annotations 12a, 12b, 12c, 12d to the images of the four cracks 11a, 11b, 11c, and 11d, respectively.
  • the three images of 11a, 11b, and 11c are each divided into a plurality of images, and the divided images of the plurality of cracks are obtained.
  • the image of the crack 11a is divided into three parts, and annotations 12a1, 12a2, and 12a3 are added to each of them to generate three learning data.
  • the number of annotated training data can be increased, so that the generalization performance and accuracy of the defect detector can be improved.
  • the learning device 100 may accept the designation of the annotation from the person in charge, or may automatically or semi-automatically add the annotation.
  • the learning device 100 receives the designation of the position, size, and type of the defect included in the captured image 10 from the person in charge, and divides the image of the defect according to the size, type, number, and the like of the designated defect. Whether to generate the training data from the data or to generate the training data without dividing it, and in the case of dividing, the number of divisions and the size and shape of each divided region may be determined. For example, since the crack 11d shown in FIG. 2A has a monotonous shape regardless of the position, the annotation 12d may be added to the entire crack without dividing it. Since the cracks 11a, 11b, and 11c have various shapes, they may be divided into a number according to the size and shape of the cracks and annotated with each of them.
  • FIG. 3 shows an example of a captured image of the surface of the tank 2.
  • FIG. 3A shows an image of a crack formed on the surface of the tank 2.
  • the captured image of the surface of the tank 2 may include the contrast of brightness caused by defects such as cracks, as well as the contrast of brightness caused by the unevenness of the surface of the tank 2 and the ambient light at the time of imaging. If the defect detector mislearns such features of the background image that should not be detected, the detection accuracy may decrease.
  • the captured image is subjected to image processing before the learning data is generated from the captured image to be inspected.
  • This preprocessing may be an image process capable of reducing the contrast of an image of a portion other than the detection target portion such as a defect to smooth the brightness.
  • the blur filter is, for example, a box-shaped filter that averages the pixel values within the range of the kernel, a Gaussian filter that changes the weight according to the distance to the pixel of interest, and a median that adopts the median value of all pixels within the range of the kernel. It may be a filter, a bilateral filter which is a Gaussian filter with a weight of a normal distribution, or the like.
  • the preprocessing may be image processing that lowers the resolution of the image. As a result, the learning efficiency can be improved by simple image processing, so that a highly accurate defect detector can be generated in a short period of time.
  • FIG. 3B shows an image obtained by applying a blur filter to the image shown in FIG. 3A. Since the background is blurred and the contrast is reduced, it is possible to reduce erroneous learning of background features. The contrast of the defects is also slightly reduced, but the characteristics are preserved so that the defect detector can learn the characteristics of the defects.
  • the image processing may be performed under conditions such that the contrast characteristics in the detection target portion are not lost and the contrast in the non-detection target portion is sufficiently smoothed.
  • the learning device 100 acquires the luminance distribution in the detection target portion and the luminance distribution in the background other than the detection target portion, and determines conditions such as the type of image processing and the kernel size based on the respective luminance distributions. You may. When the learning device 100 inspects a plurality of captured images obtained by capturing the same type of inspection target in the same environment, the learning device 100 may perform image processing under the same conditions.
  • the imaging may be performed in an imaging environment in which the contrast of the non-defect portion is sufficiently low.
  • the tank 2 may be irradiated with light with an illuminance corresponding to the color and reflectance of the surface of the tank 2. Further, the focal length, resolution, aperture and the like of the image pickup apparatus 3 may be adjusted.
  • FIG. 4 is a flowchart showing the procedure of the learning method according to the embodiment.
  • the learning device 100 acquires a captured image of an inspection target having a defect (S10), and performs preprocessing such as blurring on the acquired captured image (S12).
  • the learning device 100 divides at least a part of the defect image included in the captured image into a plurality of parts (S14), adds annotations to each of the divided plurality of defect images, and generates learning data (S16).
  • the learning device 100 may add different annotations for each type of defect. As a result, the characteristics can be learned by the defect detector for each type of defect, so that the defect can be detected from the captured image to be inspected and the type of defect can be determined at the same time.
  • the learning device 100 learns the defect detector by supervised learning using the generated learning data.
  • the learning device 100 may apply any known learning algorithm when learning the defect detector.
  • FIG. 5 is a flowchart showing the procedure of the inspection method according to the embodiment.
  • the inspection device 200 acquires the captured image to be inspected (S30), and performs the same image processing on the captured image as that performed when the learning device 100 generates the learning data (S32).
  • the inspection device 200 detects a defect from the captured image using the learned defect detector (S34), and outputs the detection result (S36).
  • the present inventor learns the defect detector by using the captured images of 212 rejected products of the tank 2 including defects by the above method, and uses the learned defect detector to learn the passed product 500 of the tank 2. Inspection of points and 23 points of rejected products was carried out. The number of cases in which a rejected product was erroneously judged as a passed product was 0. As a result, it was shown that the technique of the present embodiment can minimize the possibility of erroneously shipping the rejected product. In addition, the number of cases in which the accepted product was erroneously determined as the rejected product was 7, and the correct answer rate was 98.6%. As a result, it was shown that the time and effort required for the inspector to confirm the rejected product can be significantly reduced, and the rate at which the accepted product cannot be shipped and is lost can be significantly reduced.
  • FIG. 6 shows the configuration of the learning device 100 according to the embodiment.
  • the learning device 100 includes a display device 112, an input device 113, a communication device 114, a processing device 120, and a storage device 130.
  • the learning device 100 may be a server device, a device such as a personal computer, or a mobile terminal such as a mobile phone terminal, a smartphone, or a tablet terminal.
  • the display device 112 displays the screen generated by the processing device 120.
  • the display device 112 may be a liquid crystal display device, an organic EL display device, or the like.
  • the input device 113 transmits the instruction input by the user of the learning device 100 to the processing device 120.
  • the input device 113 may be a mouse, a keyboard, a touch pad, or the like.
  • the display device 112 and the input device 113 may be mounted as a touch panel.
  • the communication device 114 controls communication with other devices.
  • the communication device 114 may perform communication by any wired or wireless communication method.
  • the communication device 114 communicates with the image pickup device 3 and the inspection device 200 via the Internet 4.
  • the storage device 130 stores programs, data, and the like used by the processing device 120.
  • the storage device 130 may be a semiconductor memory, a hard disk, or the like.
  • the storage device 130 stores a learning image holding unit 131, a learning data holding unit 132, and a defect detector 133.
  • the processing device 120 includes a learning image acquisition unit 121, an image processing unit 122, a learning data generation unit 123, a learning unit 124, and a defect detector providing unit 125. These configurations are realized by the CPU, memory, and other LSIs of any computer in terms of hardware, and are realized by programs loaded in memory in terms of software. It depicts a functional block realized by. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms such as hardware alone or a combination of hardware and software.
  • the learning image acquisition unit 121 acquires an captured image of an inspection target having a defect from the image pickup device 3 and stores it in the learning image holding unit 131.
  • the image processing unit 122 performs image processing such as blurring on the learning image stored in the learning image holding unit 131.
  • the learning data generation unit 123 annotates defects included in the learning image.
  • the learning data generation unit 123 divides at least a part of the defective images into a plurality of parts, and annotates each of the divided defective images.
  • the learning data generation unit 123 may display the learning image on the display device 112 and accept the designation of the annotation from the person in charge via the input device 113.
  • the learning data generation unit 123 may automatically add annotations to defects.
  • the learning data generation unit 123 generates learning data and stores it in the learning data holding unit 132.
  • the learning unit 124 learns the defect detector 133 using the learning data stored in the learning data holding unit 132.
  • the defect detector providing unit 125 provides the trained defect detector 133 to the inspection device 200.
  • FIG. 7 shows the configuration of the inspection device 200 according to the embodiment.
  • the inspection device 200 includes a display device 212, an input device 213, a communication device 214, a processing device 220, and a storage device 230.
  • the inspection device 200 may be a server device, a device such as a personal computer, or a mobile terminal such as a mobile phone terminal, a smartphone, or a tablet terminal.
  • the display device 212 displays the screen generated by the processing device 220.
  • the display device 212 may be a liquid crystal display device, an organic EL display device, or the like.
  • the input device 213 transmits an instruction input by the user of the inspection device 200 to the processing device 220.
  • the input device 213 may be a mouse, a keyboard, a touch pad, or the like.
  • the display device 212 and the input device 213 may be mounted as a touch panel.
  • Communication device 214 controls communication with other devices.
  • the communication device 214 may perform communication by any wired or wireless communication method.
  • the communication device 214 communicates with the image pickup device 3 and the learning device 100 via the Internet 4.
  • the storage device 230 stores programs, data, etc. used by the processing device 220.
  • the storage device 230 may be a semiconductor memory, a hard disk, or the like.
  • the storage device 230 stores an inspection image holding unit 231, a detection result holding unit 232, and a defect detector 233.
  • the processing device 220 includes an inspection image acquisition unit 221, an image processing unit 222, a defect detection unit 223, a detection result generation unit 224, and a detection result output unit 225. These configurations can also be realized in various forms such as hardware alone or a combination of hardware and software.
  • the inspection image acquisition unit 221 acquires the image of the inspection target captured by the image pickup device 3 from the image pickup device 3 and stores it in the inspection image holding unit 231.
  • the image processing unit 222 performs the same image processing on the inspection image as that performed on the learning image by the learning device 100.
  • the defect detection unit 223 detects defects from the inspection image stored in the inspection image holding unit 231 by using the learned defect detector 233 acquired from the learning device 100, and detects the detection result in the detection result holding unit 232. Store in.
  • the detection result generation unit 224 generates information on the detected defects. When a plurality of defects detected by the defect detection unit 223 are close to each other, the detection result generation unit 224 combines the defects into one defect.
  • the detection result generation unit 224 generates information such as the position, size, number, and type of the detected defects.
  • the detection result output unit 225 outputs the detection result generated by the detection result generation unit 224 to the display device 212 or the like.
  • the technique of detecting cracks generated on the surface of the sanitary ware has been mainly described, but the technique of the present embodiment has another kind of defect occurring on the surface of the sanitary ware, for example, iron. It can also be applied to detect the adhesion of copper, substrate, foreign matter, etc., abnormalities such as glaze and color, and the occurrence of bubbles, cracks, chips, etc. Further, any product other than sanitary ware may be the inspection target, or any detection target site other than the defect may be the detection target.
  • the present disclosure relates to a technique for inspecting an inspection target, and in particular, an inspection system for inspecting an inspection target, a learning device available for the inspection system, a learning program, a learning method, and an inspection device available for the inspection system. Regarding inspection programs and inspection methods.
  • 1 inspection system 2 tanks, 3 imaging devices, 4 internet, 10 captured images, 11 cracks, 12 annotations, 100 learning devices, 121 learning image acquisition units, 122 image processing units, 123 learning data generation units, 124 learning units, 125 Defect detector providing unit, 131 Learning image holding unit, 132 Learning data holding unit, 133 Defect detector, 200 Inspection device, 221 Inspection image acquisition unit, 222 Image processing unit, 223 Defect detection unit, 224 Detection result generation 225 Detection result output unit, 231 Inspection image holding unit, 232 Detection result holding unit, 233 Defect detector.

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Abstract

Un système d'inspection selon l'invention comprend : un dispositif d'apprentissage 100 qui effectue l'apprentissage d'un détecteur pour détecter une partie cible de détection d'un réservoir 2, qui est une cible d'inspection ; et un dispositif d'inspection 200 qui détecte la partie cible de détection du réservoir 2 à l'aide du détecteur qui a subi l'apprentissage par le dispositif d'apprentissage 100. Le dispositif d'apprentissage 100 comprend : une unité d'acquisition d'image d'apprentissage qui acquiert une image prise de la cible d'inspection comprenant la partie cible de détection ; une unité de génération de données d'apprentissage qui génère des données d'apprentissage à partir de l'image de la partie cible de détection incluse dans l'image prise de la cible d'inspection acquise par l'unité d'acquisition d'image d'apprentissage ; et une unité d'apprentissage qui réalise l'apprentissage du détecteur à l'aide des données d'apprentissage générées par l'unité de génération de données d'apprentissage. L'unité de génération de données d'apprentissage divise au moins certaines images d'une pluralité d'images de la partie cible de détection en une pluralité d'images, et génère des données d'apprentissage à partir de chacune des images divisées de la partie cible de détection.
PCT/JP2021/003830 2020-03-23 2021-02-03 Système d'inspection, dispositif d'apprentissage, programme d'apprentissage, procédé d'apprentissage, dispositif d'inspection, programme d'inspection et procédé d'inspection WO2021192627A1 (fr)

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JP2020-051136 2020-03-23
JP2020051136A JP7449739B2 (ja) 2020-03-23 2020-03-23 検査システム、学習装置、学習プログラム、学習方法、検査装置、検査プログラム、検査方法
JP2020-051135 2020-03-23
JP2020051135A JP2021148719A (ja) 2020-03-23 2020-03-23 検査システム、学習装置、学習プログラム、学習方法、検査装置、検査プログラム、検査方法

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JP6294529B1 (ja) * 2017-03-16 2018-03-14 阪神高速技術株式会社 ひび割れ検出処理装置、およびひび割れ検出処理プログラム
KR101926561B1 (ko) * 2018-03-13 2018-12-07 연세대학교 산학협력단 블랙박스 영상을 이용한 딥러닝 기반의 패치 단위 도로 크랙 검출 장치 및 그 방법, 그리고 이 방법을 실행시키기 위해 컴퓨터가 판독 가능한 기록매체에 저장된 컴퓨터 프로그램

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