WO2023084849A1 - Inspecting method and inspecting device - Google Patents

Inspecting method and inspecting device Download PDF

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Publication number
WO2023084849A1
WO2023084849A1 PCT/JP2022/029623 JP2022029623W WO2023084849A1 WO 2023084849 A1 WO2023084849 A1 WO 2023084849A1 JP 2022029623 W JP2022029623 W JP 2022029623W WO 2023084849 A1 WO2023084849 A1 WO 2023084849A1
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inspection
contour
temporary
image
area
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French (fr)
Japanese (ja)
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恭弘 藤岡
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日立Astemo株式会社
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation

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  • the present invention relates to an inspection method and an inspection apparatus.
  • Japanese Patent Laid-Open No. 2002-200000 discloses detecting an outer edge and an effective area of a peripheral area of an element chip based on an image of the element chip captured by an imaging unit in order to suppress overlooking of a defect or erroneous detection of a defect. Then, an inspection area for inspecting defects of the element chip is determined based on the detected outer edge of the peripheral area and the effective area, and an image corresponding to the inspection area of the element chip and a previously stored A defect inspection apparatus is disclosed that detects defects in element chips by comparing images of non-defective element chips.
  • Patent Document 1 does not consider erroneously recognizing the edge outside the peripheral area and the effective area. There is a risk of erroneously recognizing an inspection area for inspecting defects in an element chip.
  • An inspection method includes an inspection image acquiring step of acquiring an inspection image of an object to be inspected; dividing the inspection image into a temporary inspection area and a temporary non-inspection area based on the characteristics of the inspection image; a first contour obtaining step of obtaining a boundary line between a provisional inspection region and a provisional non-inspection region as a first contour; , the specific portion of the first contour is corrected with the reference feature to obtain the second contour, and based on the second contour, and an inspection step of dividing an inspection image into an inspection area and a non-inspection area and inspecting the inspection area.
  • inspection accuracy can be improved by generating accurate inspection areas for each non-inspection object.
  • FIG. 1 is a schematic diagram of an inspection apparatus according to Embodiment 1;
  • FIG. 4 is a flow chart showing the flow of an inspection method according to Embodiment 1.
  • FIG. 5 is a time chart for explaining the operation of the inspection method according to Embodiment 1.
  • FIG. 10 is a flow chart showing the flow of an inspection method according to Embodiment 2.
  • FIG. 9 is a time chart for explaining the operation of the inspection method according to Embodiment 3; 14 is a time chart for explaining the operation of the second contour acquisition step in Embodiment 4.
  • FIG. 13 is a time chart for explaining the operation of the inspection method according to Embodiment 5.
  • FIG. 5 is a time chart for explaining the operation of the inspection method according to Embodiment 1.
  • FIG. 5 is a time chart for explaining the operation of the inspection method according to Embodiment 1.
  • FIG. 10 is a flow chart showing the flow of an inspection method according to Embodiment 2.
  • FIG. 9 is a time chart for explaining the operation
  • FIG. 1 is a schematic diagram of an inspection apparatus 1 of Embodiment 1.
  • An inspection apparatus 1 of Embodiment 1 includes a camera (inspection image acquisition unit) 2 , a robot 3 and a computer 4 .
  • the camera 2 captures an image of the inspection surface of the piston (object to be inspected) 5 (inspection image acquisition step).
  • the robot 3 changes the attitude angle of the piston 5 with respect to the camera 2 .
  • the computer 4 is a personal computer, for example, and has a memory 6 and a CPU 7 .
  • the CPU 7 includes a first contour acquisition section 8 , a second contour acquisition section 9 and an inspection section 10 .
  • the memory 6 stores learning results of machine learning using a plurality of sample images.
  • Machine learning is, for example, learning using a neural network, and in the first embodiment, learning by deep learning is adopted.
  • learning is performed by deep learning in which neural networks are connected in multiple layers, it is possible to improve the judgment accuracy of inspection compared to the case where neural networks are used.
  • the memory 6 stores two-dimensional or three-dimensional CAD data for design (shape data, reference features), dimensional data of non-defective pistons (shape data, reference features), predetermined curvature radii (reference and data such as a predetermined size (reference feature), etc. are stored. Details will be described later.
  • FIG. 2 is a flow chart showing the flow of the inspection method according to the first embodiment.
  • step S1 an inspection image of the piston 5 is acquired by the camera 2 (inspection image acquiring step).
  • step S2 the outline is extracted based on the pattern of the inspection image or the brightness change of the pixels, and divided into a temporary inspection area Ta and a temporary non-inspection area Ja (first outline acquisition step).
  • step S3 the boundary line between the temporary inspection area Ta and the temporary inspection area Ja is obtained as a first contour R1 (first contour obtaining step).
  • step S4 the first contour R1 is compared with the CAD data D1 (shape data, reference features), which is two-dimensional or three-dimensional design shape data stored in advance in the memory 6, to obtain a second contour R2a. (second contour acquisition step).
  • CAD data D1 shape data, reference features
  • step S5 it is divided into an inspection region Tr and a non-inspection region Jr based on the second contour R2a (second contour obtaining step).
  • step S6 the inspection area Tr based on the corrected second contour R2a is inspected by inference based on deep learning of machine learning to extract the defect location. (inspection step).
  • FIG. 3 is a time chart explaining the operation of the inspection method according to the first embodiment.
  • an inspection image of the piston 5 is acquired by the camera 2 .
  • the enlarged inspection image of the arrow A portion will be described below.
  • the contour is extracted based on the pattern of the inspection image or the luminance change of the pixels, and divided into a temporary inspection area Ta and a temporary non-inspection area Ja.
  • a boundary line of the inspection area Ja is obtained as a first contour R1 indicated by a dashed line. Since the non-inspection area Ja and the defect K are similar in pattern or pixel brightness, the area including the defect K is erroneously recognized as the non-inspection area Ja.
  • the CAD data D1 and the first contour R1 are compared, and the portion where the length between the CAD data D1 and the first contour R1 is equal to or greater than a predetermined length a0 is determined as the specified portion Q (a1>). a0).
  • the first contour R1 is modified to obtain a second contour R2a, and the inspection image is divided into an inspection region Tr and a non-inspection region Jr based on the second contour R2a.
  • the inspection area Tr based on the corrected second contour R2a is inspected by inference based on deep learning of machine learning to extract the defect location. Each of these steps is performed for each individual piston to be inspected.
  • the erroneous recognition of the temporary inspection area Ta and the temporary non-inspection area Ja is corrected with the CAD data D1, which is the shape data for two-dimensional or three-dimensional design, thereby obtaining the second contour R2a, and the actual Since it is possible to obtain an accurate inspection area that matches the inspection image of the above, it is possible to improve the inspection accuracy of each piston to be inspected.
  • the inspection apparatus 1 performs an inspection image acquiring step of acquiring an inspection image of the piston 5, and divides the temporary inspection area Ta and the temporary non-inspection area Ja into the temporary inspection area Ta and the temporary non-inspection area Ja.
  • CAD data D1 which is shape data for two-dimensional or three-dimensional design pre-stored in the memory 6
  • a portion where the length between the CAD data D1 and the first contour R1 is equal to or greater than a predetermined length a0 is extracted as a specific portion Q (a1>a0), the specific portion Q is removed, the first contour R1 is corrected, and the first contour R1 is corrected.
  • the first contour R1 is compared with the CAD data D1, which is shape data for two-dimensional or three-dimensional design stored in the memory 6 in advance. Therefore, they can be easily compared.
  • the outline is extracted based on the pattern of the inspection image or the brightness change of the pixels, and divided into a temporary inspection area Ta and a temporary non-inspection area Ja. Therefore, it is possible to easily obtain a temporary inspection area from an actual inspection image.
  • FIG. 4 is a flow chart showing the flow of the inspection method according to the second embodiment.
  • the outline is extracted based on the pattern of the inspection image or the brightness change of the pixels, and divided into the temporary inspection area Ta and the temporary non-inspection area Ja. Based on the acquired feature amount, the area is divided into a temporary inspection area Ta and a temporary non-inspection area Ja.
  • the CAD data D1 which is shape data for two-dimensional or three-dimensional design. Therefore, it is possible to acquire an accurate inspection area that matches the actual inspection image without changing the logic of machine learning, so it is possible to improve the inspection accuracy of each piston to be inspected. Since other configurations are the same as those of the first embodiment, the same configurations are denoted by the same reference numerals, and descriptions thereof are omitted. Therefore, the second embodiment has the same effect as the first embodiment.
  • FIG. 5 is a time chart explaining the operation of the inspection method according to the third embodiment.
  • the second contour R2a is obtained by correcting the erroneous recognition of the temporary inspection area Ta and the temporary non-inspection area Ja with the CAD data D1, which is shape data for two-dimensional or three-dimensional design.
  • CAD data D1 shape data for two-dimensional or three-dimensional design.
  • misrecognition of the provisional inspection area Ta and the provisional non-inspection area Ja is corrected with the dimensional data (shape data, reference features) D2 extracted from the non-defective piston, which is close to the actual product.
  • the second contour R2b is obtained. Thereby, inspection accuracy can be further improved. Since other configurations are the same as those of the first embodiment, the same configurations are denoted by the same reference numerals, and descriptions thereof are omitted.
  • FIG. 6 is a time chart explaining the operation of the second contour acquisition step in the fourth embodiment.
  • the second contour R2a is obtained by correcting the erroneous recognition of the temporary inspection area Ta and the temporary non-inspection area Ja with the CAD data D1, which is shape data for two-dimensional or three-dimensional design.
  • the second contour R2c is obtained by repeating reconnecting the adjacent points f.
  • the specific portion Q in which the radius of curvature r1 of the first contour R1 is extremely small is highly likely to be erroneously recognized, so that the inspection region Tr can be determined with high accuracy. Since other configurations are the same as those of the first embodiment, the same configurations are denoted by the same reference numerals, and descriptions thereof are omitted. Therefore, the fourth embodiment has the same effect as the first embodiment.
  • FIG. 7 is a time chart explaining the operation of the inspection method according to the fifth embodiment.
  • the inspection method of the first embodiment comprises the first contour acquisition step, the second contour acquisition step, and the inspection step.
  • the first step of dividing the inspection area Ta and the temporary non-inspection area Ja and the first part Q1 of the temporary non-inspection area Ja within the range of the temporary inspection area Ta the first part Q1 is preliminarily is smaller than a predetermined pixel (predetermined size) set and stored in advance, for example, a predetermined diameter L1.
  • the fifth embodiment has the same effect as the first embodiment.
  • the object to be inspected is not limited to a piston
  • machine learning is not limited to neural networks or deep learning.
  • the posture control section changes the posture of the piston by the robot, it may be changed by changing the position of the camera.
  • attitude control may not be performed.
  • the inspection step may be normal rule-based (dimensional criteria) rather than machine learning.
  • the predetermined pixel is not limited to the diameter, and may be the area.
  • the present invention is not limited to the above-described embodiments, and includes various modifications.
  • the above-described embodiments have been described in detail in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the configurations described.
  • part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment.
  • 1 inspection device 1 inspection device, 2 camera (inspection image acquisition unit), 3 robot, 4 computer, 5 piston (object to be inspected), 6 memory, 7 CPU, 8 first contour acquisition unit, 9 second contour acquisition unit, 10 inspection unit , a0 Predetermined length, D1 Design CAD data (shape data, reference feature), D2 Good piston dimension data (shape data, reference feature), D3 Predetermined radius of curvature r0 (reference feature) , D4 predetermined size L0 (reference feature), f point, Q specific part, Q1 first part, R1 first contour, R2a second contour, R2b second contour, R2c second contour, R2d second contour , Ta Temporary inspection area, Ja Temporary non-inspection area, Tr Inspection area, Jr Non-inspection area,

Abstract

This inspecting method includes: an inspection image acquisition step for acquiring an inspection image of an object being inspected; a first outline acquisition step for dividing the inspection image into a provisional inspection region and a provisional non-inspection region on the basis of a feature of the inspection region, and acquiring a boundary line between the provisional inspection region and the provisional non-inspection region, as a first outline; and an inspection step for comparing the first outline and a pre-stored feature, serving as a reference, and if there is a specific part in which the first outline is separated from the feature serving as a reference by at least a certain distance, using the feature serving as a reference to correct the specific part of the first outline, and acquiring a second outline, dividing the inspection image into an inspection region and a non-inspection region on the basis of the second outline, and inspecting the inspection region.

Description

検査方法および検査装置Inspection method and inspection device
　本発明は、検査方法および検査装置に関する。 The present invention relates to an inspection method and an inspection apparatus.
　特許文献１には、欠陥の見逃し、または、欠陥の誤検出を抑制するために、撮像部によって撮像された素子チップの画像に基づいて、素子チップの周縁領域の外側のエッジおよび有効領域を検出し、検出された周縁領域の外側のエッジと有効領域とに基づいて、素子チップの欠陥を検査するための検査領域を決定し、素子チップの検査領域に対応する画像と、予め記憶されている良品の素子チップの画像とを比較することにより、素子チップの欠陥を検出する欠陥検査装置が開示されている。 Japanese Patent Laid-Open No. 2002-200000 discloses detecting an outer edge and an effective area of a peripheral area of an element chip based on an image of the element chip captured by an imaging unit in order to suppress overlooking of a defect or erroneous detection of a defect. Then, an inspection area for inspecting defects of the element chip is determined based on the detected outer edge of the peripheral area and the effective area, and an image corresponding to the inspection area of the element chip and a previously stored A defect inspection apparatus is disclosed that detects defects in element chips by comparing images of non-defective element chips.
特開２０１７－１６１２３６号公報JP 2017-161236 A
　しかしながら、特許文献1の欠陥検査装置では、周縁領域の外側のエッジおよび有効領域を誤認識してしまうことについては検討されておらず、周縁領域の外側のエッジと有効領域とに基づいて決定される素子チップの欠陥を検査するための検査領域を誤認識するおそれがあった。 However, the defect inspection apparatus of Patent Document 1 does not consider erroneously recognizing the edge outside the peripheral area and the effective area. There is a risk of erroneously recognizing an inspection area for inspecting defects in an element chip.
　本発明の目的の一つは、検査領域の誤認識を抑制した検査方法および検査装置を提供することにある。
　本発明の一実施形態における検査方法は、被検査物の検査画像を取得する検査画像取得ステップと、検査画像の特徴に基づいて検査画像を仮の検査領域と仮の非検査領域とに分け、仮の検査領域と仮の非検査領域との境界線を第１輪郭として取得する第１輪郭取得ステップと、予め記憶されている基準となる特徴と第１輪郭とを比較し、基準となる特徴に対して第１輪郭が所定長さ以上離れている特定部分が存在する場合に、第１輪郭の特定部分を基準となる特徴で修正して第２輪郭を取得し、第２輪郭に基づいて検査画像を検査領域と非検査領域とに分け、検査領域を検査する検査ステップと、を有する。 One of the objects of the present invention is to provide an inspection method and an inspection apparatus that suppress erroneous recognition of an inspection area.
An inspection method according to an embodiment of the present invention includes an inspection image acquiring step of acquiring an inspection image of an object to be inspected; dividing the inspection image into a temporary inspection area and a temporary non-inspection area based on the characteristics of the inspection image; a first contour obtaining step of obtaining a boundary line between a provisional inspection region and a provisional non-inspection region as a first contour; , the specific portion of the first contour is corrected with the reference feature to obtain the second contour, and based on the second contour, and an inspection step of dividing an inspection image into an inspection area and a non-inspection area and inspecting the inspection area.
　本発明の一実施形態によれば、非検査物個々に正確な検査領域を生成することで検査精度を向上することができる。 According to one embodiment of the present invention, inspection accuracy can be improved by generating accurate inspection areas for each non-inspection object.
実施形態１の検査装置の概略図である。1 is a schematic diagram of an inspection apparatus according to Embodiment 1; FIG. 実施形態１における検査方法の流れを示すフローチャートである。4 is a flow chart showing the flow of an inspection method according to Embodiment 1. FIG. 実施形態１における検査方法の動作を説明するタイムチャートである。5 is a time chart for explaining the operation of the inspection method according to Embodiment 1. FIG. 実施形態２における検査方法の流れを示すフローチャートである。10 is a flow chart showing the flow of an inspection method according to Embodiment 2. FIG. 実施形態３における検査方法の動作を説明するタイムチャートである。9 is a time chart for explaining the operation of the inspection method according to Embodiment 3; 実施形態４における第２輪郭取得ステップの動作を説明するタイムチャートである。14 is a time chart for explaining the operation of the second contour acquisition step in Embodiment 4. FIG. 実施形態５における検査方法の動作を説明するタイムチャートである。13 is a time chart for explaining the operation of the inspection method according to Embodiment 5. FIG.
　〔実施形態１〕
　図１は、実施形態１の検査装置1の概略図である。
　実施形態１の検査装置1は、カメラ（検査画像取得部）２、ロボット３およびコンピュータ４を備える。
　カメラ２は、ピストン（被検査物）５の検査用表面の画像を撮像する（検査画像取得ステップ）。
　ロボット３は、カメラ２に対するピストン５の姿勢角を変化させる。
　コンピュータ４は、例えばパーソナルコンピュータであり、メモリ６およびＣＰＵ７を備える。
　ＣＰＵ７は、第１輪郭取得部８と第２輪郭取得部９と検査部１０を備える。
　メモリ６には、複数のサンプル画像を用いて機械学習させた学習結果が記憶されている。
　機械学習は、例えば、ニューラルネットを用いた学習であって、実施形態１では、ディープラーニングによる学習を採用している。
　また、ニューラルネットを多層に結合したディープラーニングによる学習を行うため、ニューラルネットを用いた場合と比較して、検査の判定精度を向上できる。
　さらに、メモリ６には、２次元または３次元の設計用のＣＡＤデータ（形状データ、基準となる特徴）、良品ピストンの寸法データ（形状データ、基準となる特徴）、所定の曲率半径（基準となる特徴）、所定の大きさ（基準となる特徴）等のデータが記憶されている。詳細は、後述する。 [Embodiment 1]
FIG. 1 is a schematic diagram of an inspection apparatus 1 of Embodiment 1. FIG.
An inspection apparatus 1 of Embodiment 1 includes a camera (inspection image acquisition unit) 2 , a robot 3 and a computer 4 .
The camera 2 captures an image of the inspection surface of the piston (object to be inspected) 5 (inspection image acquisition step).
The robot 3 changes the attitude angle of the piston 5 with respect to the camera 2 .
The computer 4 is a personal computer, for example, and has a memory 6 and a CPU 7 .
The CPU 7 includes a first contour acquisition section 8 , a second contour acquisition section 9 and an inspection section 10 .
The memory 6 stores learning results of machine learning using a plurality of sample images.
Machine learning is, for example, learning using a neural network, and in the first embodiment, learning by deep learning is adopted.
In addition, since learning is performed by deep learning in which neural networks are connected in multiple layers, it is possible to improve the judgment accuracy of inspection compared to the case where neural networks are used.
Furthermore, the memory 6 stores two-dimensional or three-dimensional CAD data for design (shape data, reference features), dimensional data of non-defective pistons (shape data, reference features), predetermined curvature radii (reference and data such as a predetermined size (reference feature), etc. are stored. Details will be described later.
　図２は、実施形態1における検査方法の流れを示すフローチャートである。 FIG. 2 is a flow chart showing the flow of the inspection method according to the first embodiment.
　ステップＳ１では、カメラ２によりピストン５の検査画像を取得する（検査画像取得ステップ）。
　ステップＳ２では、検査画像の模様若しくは画素の輝度変化に基づいて輪郭を抽出し、仮の検査領域Ｔａと仮の非検査領域Ｊａに分ける（第１輪郭取得ステップ）。
　ステップＳ３では、仮の検査領域Ｔａと仮の被検査領域Ｊａの境界線を第1輪郭Ｒ１として取得する（第１輪郭取得ステップ）。
　ステップＳ４では、メモリ６に予め記憶されている２次元または３次元の設計用の形状データであるＣＡＤデータＤ１（形状データ、基準となる特徴）と第1輪郭Ｒ１を比較し、第２輪郭Ｒ２ａを取得する（第２輪郭取得ステップ）。
　このように、２次元または３次元の設計用の形状データであるＣＡＤデータＤ１を使うことにより、簡易に比較することができる。
　ステップＳ５では、第２輪郭Ｒ２ａに基づいて検査領域Ｔｒと非検査領域Ｊｒに分ける（第２輪郭取得ステップ）。
　ステップＳ６では、修正された第２輪郭Ｒ２ａに基づく検査領域Ｔｒを機械学習のディープラーニングに基づく推論により検査し、欠陥個所を抽出する。（検査ステップ）。 In step S1, an inspection image of the piston 5 is acquired by the camera 2 (inspection image acquiring step).
In step S2, the outline is extracted based on the pattern of the inspection image or the brightness change of the pixels, and divided into a temporary inspection area Ta and a temporary non-inspection area Ja (first outline acquisition step).
In step S3, the boundary line between the temporary inspection area Ta and the temporary inspection area Ja is obtained as a first contour R1 (first contour obtaining step).
In step S4, the first contour R1 is compared with the CAD data D1 (shape data, reference features), which is two-dimensional or three-dimensional design shape data stored in advance in the memory 6, to obtain a second contour R2a. (second contour acquisition step).
In this way, by using the CAD data D1, which is shape data for two-dimensional or three-dimensional design, comparison can be easily performed.
In step S5, it is divided into an inspection region Tr and a non-inspection region Jr based on the second contour R2a (second contour obtaining step).
In step S6, the inspection area Tr based on the corrected second contour R2a is inspected by inference based on deep learning of machine learning to extract the defect location. (inspection step).
　図３は、実施形態１における検査方法の動作を説明するタイムチャートである。 FIG. 3 is a time chart explaining the operation of the inspection method according to the first embodiment.
　まず、検査画像取得ステップでは、カメラ２によりピストン５の検査画像を取得する。
　なお、以下矢視Ａ部の拡大検査画像で説明する。
　つぎに、第１輪郭取得ステップでは、検査画像の模様若しくは画素の輝度変化に基づいて輪郭を抽出し、仮の検査領域Ｔａと仮の非検査領域Ｊａに分け、仮の検査領域Ｔａと仮の被検査領域Ｊａの境界線を、破線で示す第1輪郭Ｒ１として取得する。
　なお、非検査領域Ｊａと欠陥Ｋの模様若しくは画素の輝度は、類似しているため、欠陥Ｋを含む領域を非検査領域Ｊａとして誤認識している。
　このため、第２輪郭取得ステップでは、ＣＡＤデータＤ１と第1輪郭Ｒ１を比較し、ＣＡＤデータＤ１と第1輪郭Ｒ１間の長さが、所定長さａ０以上の箇所を特定部分Ｑ（ａ１＞ａ０）として抽出する。
　さらに、特定部分Ｑを除き、第1輪郭Ｒ１を修正して、第２輪郭Ｒ２ａを取得し、第２輪郭Ｒ２ａに基づいて検査画像を検査領域Ｔｒと非検査領域Ｊｒに分ける。
　検査ステップでは、修正された第２輪郭Ｒ２ａに基づく検査領域Ｔｒを機械学習のディープラーニングに基づく推論により検査し、欠陥個所を抽出する。
　この各ステップを検査対象の個々のピストンに対して実施する。
　これにより、仮の検査領域Ｔａと仮の非検査領域Ｊａの誤認識を、２次元または３次元の設計用の形状データであるＣＡＤデータＤ１で修正することで第２輪郭Ｒ２ａを取得し、実際の検査画像に合った正確な検査領域を取得することができるので、検査対象のピストン個々の検査精度を向上することができる。 First, in the inspection image acquisition step, an inspection image of the piston 5 is acquired by the camera 2 .
In addition, the enlarged inspection image of the arrow A portion will be described below.
Next, in the first contour obtaining step, the contour is extracted based on the pattern of the inspection image or the luminance change of the pixels, and divided into a temporary inspection area Ta and a temporary non-inspection area Ja. A boundary line of the inspection area Ja is obtained as a first contour R1 indicated by a dashed line.
Since the non-inspection area Ja and the defect K are similar in pattern or pixel brightness, the area including the defect K is erroneously recognized as the non-inspection area Ja.
For this reason, in the second contour acquisition step, the CAD data D1 and the first contour R1 are compared, and the portion where the length between the CAD data D1 and the first contour R1 is equal to or greater than a predetermined length a0 is determined as the specified portion Q (a1>). a0).
Furthermore, except for the specific portion Q, the first contour R1 is modified to obtain a second contour R2a, and the inspection image is divided into an inspection region Tr and a non-inspection region Jr based on the second contour R2a.
In the inspection step, the inspection area Tr based on the corrected second contour R2a is inspected by inference based on deep learning of machine learning to extract the defect location.
Each of these steps is performed for each individual piston to be inspected.
As a result, the erroneous recognition of the temporary inspection area Ta and the temporary non-inspection area Ja is corrected with the CAD data D1, which is the shape data for two-dimensional or three-dimensional design, thereby obtaining the second contour R2a, and the actual Since it is possible to obtain an accurate inspection area that matches the inspection image of the above, it is possible to improve the inspection accuracy of each piston to be inspected.
　次に、実施形態１の作用効果を説明する。 Next, the effects of Embodiment 1 will be described.
（１）検査装置１は、ピストン５の検査画像を取得する検査画像取得ステップと、仮の検査領域Ｔａと仮の非検査領域Ｊａに分け、仮の検査領域Ｔａと仮の非検査領域Ｊａの境界線を第１輪郭Ｒ１として取得する第１輪郭取得ステップと、メモリ６に予め記憶されている２次元または３次元の設計用の形状データであるＣＡＤデータＤ１と第1輪郭Ｒ１を比較し、ＣＡＤデータＤ１と第1輪郭Ｒ１間の長さが、所定長さａ０以上の箇所を特定部分Ｑ（ａ１＞ａ０）として抽出し、特定部分Ｑを除き、第1輪郭Ｒ１を修正して、第２輪郭Ｒ２ａを取得し、第２輪郭Ｒ２ａに基づいて検査画像を検査領域Ｔｒと非検査領域Ｊｒに分ける第２輪郭取得ステップと、修正された第２輪郭Ｒ２ａに基づく検査領域Ｔｒを機械学習のディープラーニングにより検査する検査ステップを有するようにした。
　よって、仮の検査領域Ｔａと仮の非検査領域Ｊａの誤認識を、２次元または３次元の設計用の形状データであるＣＡＤデータＤ１で修正することで第２輪郭Ｒ２ａを取得し、機械学習のロジックを変更することなく、実際の検査画像に合った正確な検査領域を取得することができるので、検査対象のピストン個々の検査精度を向上することができる。
　また、不正確な検査領域に基づき良品部と欠陥部が混在していると機械学習精度向上に時間を要する。本発明であれば、正確な検査領域が得られるため良品部と欠陥部の区別が明確となり機械学習精度向上の時間が短縮できる。 (1) The inspection apparatus 1 performs an inspection image acquiring step of acquiring an inspection image of the piston 5, and divides the temporary inspection area Ta and the temporary non-inspection area Ja into the temporary inspection area Ta and the temporary non-inspection area Ja. A first contour obtaining step of obtaining a boundary line as a first contour R1, and comparing the first contour R1 with CAD data D1, which is shape data for two-dimensional or three-dimensional design pre-stored in the memory 6, A portion where the length between the CAD data D1 and the first contour R1 is equal to or greater than a predetermined length a0 is extracted as a specific portion Q (a1>a0), the specific portion Q is removed, the first contour R1 is corrected, and the first contour R1 is corrected. a second contour acquisition step of acquiring two contours R2a and dividing the inspection image into an inspection region Tr and a non-inspection region Jr based on the second contour R2a; It has an inspection step that inspects by deep learning.
Therefore, the erroneous recognition of the provisional inspection area Ta and the provisional non-inspection area Ja is corrected with the CAD data D1, which is shape data for two-dimensional or three-dimensional design, to obtain the second contour R2a, and machine learning is performed. Since an accurate inspection area matching the actual inspection image can be acquired without changing the logic of , the inspection accuracy of each piston to be inspected can be improved.
In addition, if non-defective parts and defective parts are mixed based on an inaccurate inspection area, it takes time to improve the accuracy of machine learning. According to the present invention, since an accurate inspection area can be obtained, the distinction between non-defective parts and defective parts can be made clear, and the time required to improve the accuracy of machine learning can be shortened.
（２）メモリ６に予め記憶されている２次元または３次元の設計用の形状データであるＣＡＤデータＤ１と第1輪郭Ｒ１を比較するようにした。
　よって、簡易に比較することができる。 (2) The first contour R1 is compared with the CAD data D1, which is shape data for two-dimensional or three-dimensional design stored in the memory 6 in advance.
Therefore, they can be easily compared.
（３）検査画像の模様若しくは画素の輝度変化に基づいて輪郭を抽出し、仮の検査領域Ｔａと仮の非検査領域Ｊａに分けるようにした。
　よって、実際の検査画像から仮の検査領域を、簡単に取得することができる。 (3) The outline is extracted based on the pattern of the inspection image or the brightness change of the pixels, and divided into a temporary inspection area Ta and a temporary non-inspection area Ja.
Therefore, it is possible to easily obtain a temporary inspection area from an actual inspection image.
　図４は、実施形態２における検査方法の流れを示すフローチャートである。 FIG. 4 is a flow chart showing the flow of the inspection method according to the second embodiment.
　実施形態１では、検査画像の模様若しくは画素の輝度変化に基づいて輪郭を抽出し、仮の検査領域Ｔａと仮の非検査領域Ｊａに分けるようにしていたが、実施形態２では、機械学習により取得された特徴量に基づき、仮の検査領域Taと仮の非検査領域Jaに分けるようにした。
　これにより、機械学習の特徴量に基づいて得られた仮の検査領域Ｔａと仮の非検査領域Ｊａの誤認識を２次元または３次元の設計用の形状データであるＣＡＤデータＤ１で修正することで、機械学習のロジックを変更することなく、実際の検査画像に合った正確な検査領域を取得することができるので、検査対象のピストン個々の検査精度を向上することができる。
　その他の構成は、実施形態１と同じ構成であるため、同じ構成には同一符号を付して、説明は省略する。
　よって、実施形態２では、実施形態１と同様の作用効果を奏する。 In the first embodiment, the outline is extracted based on the pattern of the inspection image or the brightness change of the pixels, and divided into the temporary inspection area Ta and the temporary non-inspection area Ja. Based on the acquired feature amount, the area is divided into a temporary inspection area Ta and a temporary non-inspection area Ja.
As a result, erroneous recognition of the provisional inspection area Ta and the provisional non-inspection area Ja obtained based on the feature amount of machine learning can be corrected with the CAD data D1, which is shape data for two-dimensional or three-dimensional design. Therefore, it is possible to acquire an accurate inspection area that matches the actual inspection image without changing the logic of machine learning, so it is possible to improve the inspection accuracy of each piston to be inspected.
Since other configurations are the same as those of the first embodiment, the same configurations are denoted by the same reference numerals, and descriptions thereof are omitted.
Therefore, the second embodiment has the same effect as the first embodiment.
　図５は、実施形態３における検査方法の動作を説明するタイムチャートである。 FIG. 5 is a time chart explaining the operation of the inspection method according to the third embodiment.
　実施形態１では、仮の検査領域Ｔａと仮の非検査領域Ｊａの誤認識を、２次元または３次元の設計用の形状データであるＣＡＤデータＤ１で修正することで第２輪郭Ｒ２ａを取得するようにしていたが、実施形態３では、仮の検査領域Ｔａと仮の非検査領域Ｊａの誤認識を、良品ピストンから抽出した実物に近い寸法データ（形状データ、基準となる特徴）Ｄ２で修正することで第２輪郭Ｒ２ｂを取得するようにした。
　これにより、検査精度をより向上することができる。
　その他の構成は、実施形態１と同じ構成であるため、同じ構成には同一符号を付して、説明は省略する。 In the first embodiment, the second contour R2a is obtained by correcting the erroneous recognition of the temporary inspection area Ta and the temporary non-inspection area Ja with the CAD data D1, which is shape data for two-dimensional or three-dimensional design. However, in the third embodiment, misrecognition of the provisional inspection area Ta and the provisional non-inspection area Ja is corrected with the dimensional data (shape data, reference features) D2 extracted from the non-defective piston, which is close to the actual product. By doing so, the second contour R2b is obtained.
Thereby, inspection accuracy can be further improved.
Since other configurations are the same as those of the first embodiment, the same configurations are denoted by the same reference numerals, and descriptions thereof are omitted.
　図６は、実施形態４における第２輪郭取得ステップの動作を説明するタイムチャートである。 FIG. 6 is a time chart explaining the operation of the second contour acquisition step in the fourth embodiment.
　実施形態１では、仮の検査領域Ｔａと仮の非検査領域Ｊａの誤認識を、２次元または３次元の設計用の形状データであるＣＡＤデータＤ１で修正することで第２輪郭Ｒ２ａを取得するようにしていたが、実施形態４では、複数の点ｆを繋いで形成された第１輪郭Ｒ１の所定の曲率半径（基準となる特徴）ｒ０以下の特定部分Ｑの最も曲率半径ｒ１の小さい部分に対応する点ｆを消去し隣接する点ｆを繋ぎ直し、さらに、第１輪郭Ｒ１の所定の曲率半径ｒ０以下の特定部分Ｑの曲率半径ｒ１の小さい部分に対応する点ｆを順次消去し、隣接する点ｆを繋ぎ直すことを繰り返すことで第２輪郭Ｒ２ｃを取得するようにした。
　このように、第１輪郭Ｒ１の曲率半径ｒ１が極端に小さい特定部分Ｑは、誤認識である可能性が高いので、精度の高い検査領域Ｔｒとすることができる。
　その他の構成は、実施形態１と同じ構成であるため、同じ構成には同一符号を付して、説明は省略する。
　よって、実施形態４では、実施形態１と同様の作用効果を奏する。 In the first embodiment, the second contour R2a is obtained by correcting the erroneous recognition of the temporary inspection area Ta and the temporary non-inspection area Ja with the CAD data D1, which is shape data for two-dimensional or three-dimensional design. However, in the fourth embodiment, the portion with the smallest curvature radius r1 of the specific portion Q having a predetermined curvature radius (reference feature) r0 or less of the first contour R1 formed by connecting a plurality of points f. delete points f corresponding to and reconnect adjacent points f, and further delete points f corresponding to portions with a small curvature radius r1 of a specific portion Q of a predetermined curvature radius r0 or less of the first contour R1, The second contour R2c is obtained by repeating reconnecting the adjacent points f.
As described above, the specific portion Q in which the radius of curvature r1 of the first contour R1 is extremely small is highly likely to be erroneously recognized, so that the inspection region Tr can be determined with high accuracy.
Since other configurations are the same as those of the first embodiment, the same configurations are denoted by the same reference numerals, and descriptions thereof are omitted.
Therefore, the fourth embodiment has the same effect as the first embodiment.
　図７は、実施形態５における検査方法の動作を説明するタイムチャートである。 FIG. 7 is a time chart explaining the operation of the inspection method according to the fifth embodiment.
　実施形態１の検査方法では、第１輪郭取得ステップ、第２輪郭取得ステップ、検査ステップにより構成していたが、実施形態５では、検査画像の特徴に基づいて検査画像を第1輪郭Ｒ１により仮の検査領域Ｔａと仮の非検査領域Ｊａに分ける第１ステップと、仮の検査領域Ｔａの範囲内に仮の非検査領域Ｊａの第１部分Ｑ１が存在する場合、第１部分Ｑ１が、事前に設定され予め記憶されている所定のピクセル（所定の大きさ）、例えば、所定の直径Ｌ１よりも小さいときは、検査画像を２回収縮処理した後、２回拡張処理を行い、第１部分Ｑ１を仮の検査領域Ｊａから削除して第２輪郭Ｒ２ｄを取得し、検査領域Ｔｒと非検査領域Ｊｒを取得する第２ステップと、検査領域Ｔｒを検査する検査ステップから構成するようにした。
　これにより、簡易にノイズ除去が可能となり、実際の検査画像に合った正確な検査領域Ｔｒを取得することができるので、検査対象のピストン個々の検査精度を向上することができる。
　その他の構成は、実施形態１と同じ構成であるため、同じ構成には同一符号を付して、説明は省略する。
　よって、実施形態５では、実施形態１と同様の作用効果を奏する。 The inspection method of the first embodiment comprises the first contour acquisition step, the second contour acquisition step, and the inspection step. When the first step of dividing the inspection area Ta and the temporary non-inspection area Ja and the first part Q1 of the temporary non-inspection area Ja within the range of the temporary inspection area Ta, the first part Q1 is preliminarily is smaller than a predetermined pixel (predetermined size) set and stored in advance, for example, a predetermined diameter L1. A second step of obtaining a second contour R2d by deleting Q1 from the temporary inspection area Ja, obtaining an inspection area Tr and a non-inspection area Jr, and an inspection step of inspecting the inspection area Tr.
As a result, noise can be easily removed, and an accurate inspection region Tr that matches the actual inspection image can be obtained, so that inspection accuracy for each piston to be inspected can be improved.
Since other configurations are the same as those of the first embodiment, the same configurations are denoted by the same reference numerals, and descriptions thereof are omitted.
Therefore, the fifth embodiment has the same effect as the first embodiment.
　〔他の実施形態〕
　以上、本発明を実施するための実施形態を説明したが、本発明の具体的な構成は実施形態の構成に限定されるものではなく、発明の要旨を逸脱しない範囲の設計変更等があっても本発明に含まれる。
　例えば、被検査物は、ピストンに限らないし、機械学習は、ニューラルネットやディープラーニングに限らない。
　また、姿勢制御部は、ロボットによりピストンの姿勢変化を行っているが、カメラの位置を変更することにより行ってもよい。さらに、姿勢制御を行わなくてもよい。
　さらに、検査ステップは、機械学習ではなく、通常のルールベース（寸法基準）であってもよい。
　また、所定のピクセル（所定の大きさ）は、直径に限らず、面積であってもよい。 [Other embodiments]
Although the embodiment for carrying out the present invention has been described above, the specific configuration of the present invention is not limited to the configuration of the embodiment, and design changes, etc. within the scope of the invention may be made. is also included in the present invention.
For example, the object to be inspected is not limited to a piston, and machine learning is not limited to neural networks or deep learning.
In addition, although the posture control section changes the posture of the piston by the robot, it may be changed by changing the position of the camera. Furthermore, attitude control may not be performed.
Furthermore, the inspection step may be normal rule-based (dimensional criteria) rather than machine learning.
Also, the predetermined pixel (predetermined size) is not limited to the diameter, and may be the area.
　なお、本発明は上記した実施形態に限定されるものではなく、様々な変形例が含まれる。例えば、上記した実施形態は本発明を分かりやすく説明するために詳細に説明したものであり、必ずしも説明した全ての構成を備えるものに限定されるものではない。また、ある実施形態の構成の一部を他の実施形態の構成に置き換えることが可能であり、また、ある実施形態の構成に他の実施形態の構成を加えることも可能である。また、各実施形態の構成の一部について、他の構成の追加・削除・置換をすることが可能である。 It should be noted that the present invention is not limited to the above-described embodiments, and includes various modifications. For example, the above-described embodiments have been described in detail in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the configurations described. Also, part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Moreover, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.
　本願は、２０２１年１１月１１日付出願の日本国特許出願第２０２１－１８３９４０号に基づく優先権を主張する。２０２１年１１月１１日付出願の日本国特許出願第２０２１－１８３９４０号の明細書、特許請求の範囲、図面、および要約書を含む全開示内容は、参照により本願に全体として組み込まれる。 This application claims priority based on Japanese Patent Application No. 2021-183940 filed on November 11, 2021. The entire disclosure, including the specification, claims, drawings, and abstract of Japanese Patent Application No. 2021-183940 filed on November 11, 2021, is incorporated herein by reference in its entirety.
１　検査装置、２　カメラ（検査画像取得部）、３　ロボット、４　コンピュータ、５　ピストン（被検査物）、６　メモリ、７　ＣＰＵ、８　第１輪郭取得部、９　第２輪郭取得部、１０　検査部、ａ０　所定長さ、Ｄ１　設計用のＣＡＤデータ（形状データ、基準となる特徴）、Ｄ２　良品ピストンの寸法データ（形状データ、基準となる特徴）、Ｄ３　所定の曲率半径ｒ０（基準となる特徴）、Ｄ４　所定の大きさＬ０（基準となる特徴）、ｆ　点、Ｑ　特定部分、Ｑ1　第1部分、Ｒ１　第１輪郭、Ｒ２a　第２輪郭、Ｒ２ｂ　第２輪郭、Ｒ２ｃ　第２輪郭、Ｒ２ｄ　第２輪郭、Ｔa　仮の検査領域、Ｊa　仮の非検査領域、Ｔｒ　検査領域、Ｊｒ　非検査領域、 1 inspection device, 2 camera (inspection image acquisition unit), 3 robot, 4 computer, 5 piston (object to be inspected), 6 memory, 7 CPU, 8 first contour acquisition unit, 9 second contour acquisition unit, 10 inspection unit , a0 Predetermined length, D1 Design CAD data (shape data, reference feature), D2 Good piston dimension data (shape data, reference feature), D3 Predetermined radius of curvature r0 (reference feature) , D4 predetermined size L0 (reference feature), f point, Q specific part, Q1 first part, R1 first contour, R2a second contour, R2b second contour, R2c second contour, R2d second contour , Ta Temporary inspection area, Ja Temporary non-inspection area, Tr Inspection area, Jr Non-inspection area,

Claims (12)

  1. 　検査方法であって、
    　被検査物の検査画像を取得する検査画像取得ステップと、
    　前記検査画像の特徴に基づいて前記検査画像を仮の検査領域と仮の非検査領域とに分け、前記仮の検査領域と前記仮の非検査領域との境界線を第１輪郭として取得する第１輪郭取得ステップと、
    　予め記憶されている基準となる特徴と前記第１輪郭とを比較し、前記基準となる特徴に対して前記第１輪郭が所定長さ以上離れている特定部分が存在する場合に、前記第１輪郭の前記特定部分を前記基準となる特徴で修正して第２輪郭を取得し、前記第２輪郭に基づいて前記検査画像を検査領域と非検査領域とに分け、前記検査領域を検査する検査ステップと、を有する、
    　検査方法。     An inspection method,
    an inspection image acquiring step of acquiring an inspection image of an object to be inspected;
    dividing the inspection image into a temporary inspection area and a temporary non-inspection area based on the characteristics of the inspection image, and obtaining a boundary line between the temporary inspection area and the temporary non-inspection area as a first contour; 1 contour acquisition step;
    A pre-stored reference feature is compared with the first contour, and if there is a specific portion where the first contour is separated from the reference feature by a predetermined length or more, the first contour is detected. an inspection of obtaining a second contour by correcting the specific portion of the contour with the reference feature, dividing the inspection image into an inspection region and a non-inspection region based on the second contour, and inspecting the inspection region; having a step and
    Inspection method.
  2. 　請求項１に記載の検査方法であって、
    　前記基準となる特徴は、前記被検査物について予め設定されている形状データである、
    　検査方法。     The inspection method according to claim 1,
    The reference feature is shape data preset for the object to be inspected,
    Inspection method.
  3. 　請求項２に記載の検査方法であって、
    　前記形状データは、前記被検査物の３次元データまたは２次元データである、
    　検査方法。     The inspection method according to claim 2,
    The shape data is three-dimensional data or two-dimensional data of the object to be inspected,
    Inspection method.
  4. 　請求項２に記載の検査方法であって、
    　前記形状データは、前記被検査物以外の基準となる被検査物から抽出した寸法データである、
    　検査方法。     The inspection method according to claim 2,
    The shape data is dimension data extracted from a reference inspection object other than the inspection object,
    Inspection method.
  5. 　請求項１に記載の検査方法であって、
    　前記第１輪郭取得ステップは、前記検査画像の模様または画素の輝度変化に基づいて前記検査画像を前記仮の検査領域と前記仮の非検査領域に分ける、
    　検査方法。     The inspection method according to claim 1,
    The first contour acquisition step divides the inspection image into the temporary inspection area and the temporary non-inspection area based on a pattern of the inspection image or a luminance change of pixels.
    Inspection method.
  6. 　請求項５に記載の検査方法であって、
    　前記第１輪郭取得ステップは、事前に機械学習によって取得された特徴量に基づき、前記検査画像を前記仮の検査領域と前記仮の非検査領域に分ける、
    　検査方法。     The inspection method according to claim 5,
    In the first contour acquisition step, the inspection image is divided into the temporary inspection area and the temporary non-inspection area based on the feature amount acquired in advance by machine learning.
    Inspection method.
  7. 　請求項６に記載の検査方法であって、
    　前記機械学習は、ディープラーニングである、
    　検査方法。     The inspection method according to claim 6,
    The machine learning is deep learning,
    Inspection method.
  8. 　請求項１に記載の検査方法であって、
    　前記基準となる特徴は所定の曲率半径であり、前記第１輪郭の曲線の曲率半径が前記所定の曲率半径以下の部分を有する場合に、その部分を前記特定部分と判断する、
    　検査方法。     The inspection method according to claim 1,
    The reference feature is a predetermined radius of curvature, and if the radius of curvature of the curve of the first contour has a portion less than or equal to the predetermined radius of curvature, that portion is determined to be the specific portion.
    Inspection method.
  9. 　請求項８に記載の検査方法であって、
    　前記第１輪郭の曲線は、複数の点を繋いで形成されており、
    　前記検査ステップは、前記第１輪郭の前記特定部分の最も曲率半径の小さい部分に対応する点を消去し、隣接する点を繋ぎ直す修正ステップを有し、
    　前記検査ステップでは、前記修正ステップを繰り返して前記第２輪郭を取得する、
    　検査方法。     The inspection method according to claim 8,
    The curve of the first contour is formed by connecting a plurality of points,
    The inspection step includes a correction step of deleting a point corresponding to a portion with the smallest radius of curvature of the specific portion of the first contour and reconnecting adjacent points;
    In the inspecting step, the modifying step is repeated to obtain the second contour.
    Inspection method.
  10. 　被検査物の検査装置であって、
    　前記被検査物の検査画像を取得する検査画像取得部と、
    　前記検査画像の特徴に基づいて前記検査画像を仮の検査領域と仮の非検査領域とに分け、前記仮の検査領域と前記仮の非検査領域との境界線を第１輪郭として取得する第１輪郭取得部と、
    　予め記憶されている基準となる特徴と前記第１輪郭とを比較し、前記基準となる特徴に対して前記第１輪郭が所定長さ以上離れている特定部分が存在する場合に、前記第１輪郭の前記特定部分を前記基準となる特徴で修正して第２輪郭を取得する第２輪郭取得部と、
    　前記第２輪郭に基づいて前記検査画像を検査領域と非検査領域とに分け、前記検査領域を検査する検査部と、を有する、
    　検査装置。     An inspection device for an object to be inspected,
    an inspection image acquisition unit that acquires an inspection image of the object to be inspected;
    dividing the inspection image into a temporary inspection area and a temporary non-inspection area based on the characteristics of the inspection image, and obtaining a boundary line between the temporary inspection area and the temporary non-inspection area as a first contour; 1 contour acquisition unit;
    A pre-stored reference feature is compared with the first contour, and if there is a specific portion where the first contour is separated from the reference feature by a predetermined length or more, the first contour is detected. a second contour acquisition unit that acquires a second contour by correcting the specific portion of the contour with the reference feature;
    an inspection unit that divides the inspection image into an inspection area and a non-inspection area based on the second contour, and inspects the inspection area;
    inspection equipment.
  11. 　検査方法であって、
    　被検査物の検査画像を取得する検査画像取得ステップと、
    　前記検査画像の特徴に基づいて前記検査画像を仮の検査領域と仮の非検査領域とに分ける第１ステップと、
    　前記仮の検査領域の範囲内に前記仮の非検査領域の第１部分が存在する場合であって、前記第１部分が予め記憶されている所定の大きさよりも小さいときに、前記第１部分を前記仮の検査領域から削除して検査領域を取得する第２ステップと、
    　検査領域を検査する第３ステップと、を有する、
    　検査方法。     An inspection method,
    an inspection image acquiring step of acquiring an inspection image of an object to be inspected;
    a first step of dividing the inspection image into a temporary inspection area and a temporary non-inspection area based on features of the inspection image;
    when the first part of the temporary non-inspection area exists within the range of the temporary inspection area and the first part is smaller than a predetermined size stored in advance, the first part from the temporary inspection area to obtain an inspection area;
    a third step of inspecting the inspection area;
    Inspection method.
  12. 　請求項１１に記載の検査方法であって、
    　前記第２ステップでは、前記検査画像を収縮処理したのち、拡張処理することで前記第１部分を前記仮の検査領域から削除する、
    　検査方法。     The inspection method according to claim 11,
    In the second step, the inspection image is shrunk and then expanded to delete the first portion from the temporary inspection area.
    Inspection method.
PCT/JP2022/029623 2021-11-11 2022-08-02 Inspecting method and inspecting device WO2023084849A1 (en)

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Citations (5)

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JP2003149165A (en) * 2001-11-12 2003-05-21 Sekisui Chem Co Ltd Defect examining method
JP2011196982A (en) * 2010-03-17 2011-10-06 Cognex Kk Defect detection method, defect detection device, and program
JP2011214903A (en) * 2010-03-31 2011-10-27 Denso It Laboratory Inc Appearance inspection apparatus, and apparatus, method and program for generating appearance inspection discriminator
JP2013140090A (en) * 2012-01-05 2013-07-18 Omron Corp Inspection area setting method of image inspection device
JP2013191064A (en) * 2012-03-14 2013-09-26 Omron Corp Image inspection method and inspection area setting method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2003149165A (en) * 2001-11-12 2003-05-21 Sekisui Chem Co Ltd Defect examining method
JP2011196982A (en) * 2010-03-17 2011-10-06 Cognex Kk Defect detection method, defect detection device, and program
JP2011214903A (en) * 2010-03-31 2011-10-27 Denso It Laboratory Inc Appearance inspection apparatus, and apparatus, method and program for generating appearance inspection discriminator
JP2013140090A (en) * 2012-01-05 2013-07-18 Omron Corp Inspection area setting method of image inspection device
JP2013191064A (en) * 2012-03-14 2013-09-26 Omron Corp Image inspection method and inspection area setting method

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