WO2017181724A1 - Procédé et système de contrôle de vérification d'un composant électronique manquant - Google Patents

Procédé et système de contrôle de vérification d'un composant électronique manquant Download PDF

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WO2017181724A1
WO2017181724A1 PCT/CN2016/112888 CN2016112888W WO2017181724A1 WO 2017181724 A1 WO2017181724 A1 WO 2017181724A1 CN 2016112888 W CN2016112888 W CN 2016112888W WO 2017181724 A1 WO2017181724 A1 WO 2017181724A1
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image
edge
electronic component
edge image
test
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PCT/CN2016/112888
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English (en)
Chinese (zh)
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林建民
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广州视源电子科技股份有限公司
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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
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/0008—Industrial image inspection checking presence/absence
    • 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
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • 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
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • 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
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30141—Printed circuit board [PCB]

Definitions

  • the present invention relates to the field of optical detection technology, and in particular, to a method and system for detecting missing parts of an electronic component.
  • Optical inspection is an essential part of the industrial manufacturing process.
  • the surface state of the finished product is obtained optically, and the foreign matter or surface flaw is detected by image processing.
  • the error, leakage and back detection of electronic components is a common application in the field of circuit board defect detection.
  • the machine automatically scans the circuit board to acquire images and extracts partial images of each electronic component to determine whether the electronic components are faulty or missing. , anti-defects, and finally display or mark the components of the suspected defect for easy viewing and overhaul.
  • the detection of missing parts of an electronic component means determining whether the electronic component is inserted at a specified position, and if it is not inserted at a specified position, it is judged to be a missing piece; if it is inserted at a specified position, it is judged to be normal.
  • Method 1 the color contrast method, that is, the electronic component missing component detection is performed on the circuit board by comparing the difference of the pixel color values of the corresponding positions; the second method, the deep learning model discrimination method, that is, preparing a sample of a large number of electronic components (including positive Sample: electronic components exist; negative samples: electronic components do not exist)
  • the deep learning model is trained to detect missing parts of electronic components.
  • the existing technical solutions still have the following problems:
  • the traditional color contrast method is more susceptible to illumination changes, which causes the color information of electronic components to change, thus causing misjudgment.
  • deep learning model discrimination method requires a large number of training samples. Under the circumstance, the training samples covering all the electronic components cannot be obtained, and the missing component detection model based on the deep learning of the partial sample training is also prone to misjudgment.
  • the current electronic component miss detection method has a high false positive rate, and the quality of the printed circuit board cannot be guaranteed.
  • a method for detecting missing parts of an electronic component includes the following steps:
  • the electronic component leakage detecting is performed on the circuit board according to the similarity value.
  • the electronic component missing component detecting method obtains edge image information of the template image and the test image by performing edge image extraction on the template image and the test image of the obtained electronic component circuit board respectively;
  • the two edge images are separately partitioned, and feature extraction is performed on each partition of the first edge image and the second edge image, and global structural feature information included in the first edge image and the second edge image is acquired; and then according to the first feature parameter and the first feature parameter
  • the similarity value calculated by the two characteristic parameters is used to detect the electronic component missing parts of the circuit board.
  • An electronic component missing component detecting system includes:
  • An edge extraction module configured to perform edge image extraction on the template image and the test image of the circuit board of the acquired electronic component to obtain a first edge image and a second edge image;
  • a feature extraction module configured to separately partition the first edge image and the second edge image, and perform feature extraction on each partition of the first edge image and the second edge image to obtain a first feature parameter and a test of the template image a second characteristic parameter of the image;
  • a similarity calculation module configured to calculate a similarity value between the template image and the test image according to the first feature parameter and the second feature parameter;
  • a detecting module configured to perform electronic component missing component detection on the circuit board according to the similarity value.
  • the electronic component missing component detecting system performs edge image extraction on the template image and the test image of the obtained electronic component board by the edge extraction module to obtain edge information of the template image and the test image;
  • the first edge image and the second edge image are respectively partitioned, and feature extraction is performed on each partition of the first edge image and the second edge image to obtain a first feature parameter of the template image and a second feature parameter of the test image.
  • FIG. 1 is a flow chart of a method for detecting missing parts of an electronic component according to an embodiment of the present invention
  • FIG. 2 is a schematic structural view of an electronic component missing component detecting system according to an embodiment of the present invention
  • FIG. 3 is a schematic structural view of a feature extraction module of an electronic component missing component detecting system according to another embodiment of the present invention.
  • FIG. 1 is a flowchart of a method for detecting missing parts of an electronic component according to an embodiment of the present invention, including the following steps:
  • Step S101 performing edge image extraction on the template image and the test image of the circuit board of the acquired electronic component to obtain a first edge image and a second edge image;
  • edge image extraction is performed on the template image and the test image of the obtained electronic component board to obtain edge detail information of the template image and the test image, so as to ensure that the detection result of the subsequent step is more accurate.
  • Step S102 partitioning the first edge image and the second edge image separately, and performing feature extraction on each partition of the first edge image and the second edge image to obtain a first feature parameter of the template image and a test image.
  • the first edge image and the second edge image are separately partitioned, and feature extraction is performed on each partition of the first edge image and the second edge image to obtain a first feature parameter and a test of the template image.
  • the second characteristic parameter of the image is used to calculate the similarity value of the template image and the test image in the subsequent steps.
  • Step S103 Calculating a similarity value between the template image and the test image according to the first feature parameter and the second feature parameter;
  • the similarity values of the two are calculated according to the first feature parameter including the template image feature information and the second feature parameter including the test image, and the electronic circuit to be tested is performed according to the similarity value for the subsequent step.
  • Component missing parts detection is performed according to the similarity value for the subsequent step.
  • Step S104 Perform electronic component missing component detection on the circuit board according to the similarity value.
  • the electronic component missing component detecting method obtains edge image information of the template image and the test image by performing edge image extraction on the template image and the test image of the obtained electronic component circuit board respectively;
  • the two edge images are separately partitioned, and feature extraction is performed on each partition of the first edge image and the second edge image to obtain a first feature parameter of the template image and a second feature parameter of the test image; and then according to the first feature parameter And the similarity value calculated by the second characteristic parameter performs electronic component missing component detection on the circuit board.
  • An important information in the actual electronic component leakage detection is the edge information of the electronic component itself, and the edge structure of different electronic components is also different.
  • the edge information of the picture of the circuit board after the electronic component is missed and the electronic component inserted therein The edge information of the image of the board is also different. Therefore, the electronic component missing component detection can be performed on the circuit board to be tested according to the acquired template image of the circuit board to be tested and the edge information of the test image.
  • the electronic component missing component detecting method of the present invention performs edge image extraction on the template image and the test image of the circuit board of the acquired electronic component to obtain the first edge image and the second edge image.
  • Step S101 includes:
  • Edge image extraction is performed on the template image and the test image from three directions, respectively, to obtain a first edge image of the template image in three directions and a second edge image of the test image; wherein the three directions are respectively The horizontal direction, the vertical direction, and the diagonal direction of the template image and the test image.
  • the first edge image and the second image in the three directions of the template image and the test image are obtained by performing edge extraction on the horizontal direction, the vertical direction, and the diagonal direction of the template image and the test image, respectively.
  • Edge image is ensured that the obtained edge image contains more edge detail information, and the effectiveness of feature extraction on the obtained edge image in the subsequent steps is also ensured.
  • the electronic component missing component detecting method of the present invention performs edge image extraction on the template image and the test image from three directions to obtain a first edge of the template image in three directions.
  • the steps of the image and the second edge image of the test image may include:
  • G H represents a horizontal direction edge image of the template image or the test image
  • G V represents a vertical direction edge image of the template image or the test image
  • G D represents a diagonal direction edge image of the template image or the test image
  • S H represents The horizontal direction convolution kernel
  • S V represents the vertical direction convolution kernel
  • S D represents the diagonal direction convolution kernel
  • I represents the original template image or the test image.
  • the template image and the test image respectively obtain edge images in three directions, from the original one original image to three edge images in three directions.
  • the original template image and the test image of the socket are first obtained, and the original template image and the test image of the socket are respectively extracted in three directions, and finally the horizontal edge image and the vertical edge image of the template image are obtained.
  • the electronic component missing component detecting method of the present invention divides the first edge image and the second edge image separately, and performs each partition of the first edge image and the second edge image.
  • Feature extraction, obtaining the first feature parameter of the template image and the second feature parameter of the test image, the step S102 includes:
  • Step S1021 dividing the first edge image of the template image in three directions and the second edge image of the test image into a plurality of partitions, respectively;
  • the electronic component missing component detecting method of the present invention the step S1021 of dividing the first edge image of the template image in three directions and the second edge image of the test image into a plurality of partitions respectively Can include:
  • the first edge image of the template image in the three directions and the second edge image of the test image are respectively divided into four non-overlapping and identically sized partitions.
  • the obtained first edge image and the second edge image are respectively divided into four non-overlapping and identically-sized partitions, which effectively preserves the global structural features of the original template image and the test image, which is beneficial for subsequent
  • the accuracy of calculating the similarity values of the two steps further ensures the accuracy of the electronic component missing component detection.
  • the acquired edge image may also be divided into partitions of different sizes or overlapping each other, and the specific partitioning method may be Adjust according to the actual detection accuracy requirements and other actual needs.
  • Step S1022 Calculating local binary eigenvalues of each partition by using a local binary mode method, and combining local binary eigenvalues of each partition of the first edge image and the second edge image in a vector manner to obtain a template image.
  • the first characteristic parameter and the second characteristic parameter of the test image are the first characteristic parameter and the second characteristic parameter of the test image.
  • the Local Binary Patterns (LBP) described in the above embodiments are non-parametric operators that describe the local spatial structure of an image. It not only accurately describes detailed texture information such as points, lines, edges, but also has The advantages of translation invariance and rotation invariance; but because the local binary pattern feature is represented by a histogram, the histogram is a feature representation with weak resolution, because it is a first-order statistical feature. Ignoring the global structural features of the image, if only one histogram is generated for the entire image, important structural difference information is inevitably lost. Therefore, the electronic component missing component detecting method of the present invention passes the 3 obtained in step S101 in step S1021.
  • the edge images in the direction of the strip are divided into a plurality of partitions. In this embodiment, each edge image is divided into four partitions of the same size and not overlapping.
  • the edge image in each direction can be equally divided into four partitions, such that the first edge image in three directions of the template image is divided into 12 partitions. Similarly, the second edge image in the three directions of the test image is also divided into 12 partitions.
  • the local binary value method is used to calculate the local binary eigenvalues of the 12 partitions of the first edge image and the local binary eigenvalues of the 12 partitions of the second edge image, respectively, in step S1022.
  • the local binary eigenvalues of the 12 partitions of an edge image and the local binary eigenvalues of the 12 partitions of the second edge image are respectively combined in a vector manner to obtain a first feature parameter of the template image and a second test image.
  • Characteristic Parameters That is, the first feature parameter is a composite vector composed of 12 local binary eigenvalues of the first edge image, and the second feature parameter is a composite vector composed of 12 local binary eigenvalues of the second edge image.
  • the electronic component missing component detecting method of the present invention the calculating the similarity value of the template image and the test image according to the first feature parameter and the second feature parameter comprises:
  • the electronic component missing component detecting method of the present invention according to the first characteristic parameter And the step of calculating the similarity value between the template image and the test image by the second characteristic parameter and intersecting the histogram includes:
  • H1 and H2 represent the characteristic histogram of the first characteristic parameter and the characteristic histogram of the second characteristic parameter, respectively, and B represents the interval length of the histogram.
  • the step of performing electronic component missing component detection on the circuit board according to the similarity value comprises:
  • the step of determining whether the electronic component on the circuit board is missing according to the magnitude relationship between the similarity value and the preset threshold value comprises:
  • the electronic component missing component detection is performed on the circuit board to be tested according to the similarity value between the calculated template image and the test image.
  • R represents the similarity value between the template image and the test image
  • T represents a preset threshold
  • the electronic component missing component detecting method performs three-direction edge image extraction on the template image and the test image of the obtained electronic component's circuit board, so that the obtained edge image contains more detailed information; Separating the image and the second edge image respectively, and performing feature extraction on each of the first edge image and the second edge image to obtain a first feature parameter of the template image and a second feature parameter of the test image; The similarity value calculated by a characteristic parameter and the second characteristic parameter performs electronic component missing component detection on the circuit board.
  • FIG. 2 is a schematic structural diagram of an electronic component missing component detecting system according to an embodiment of the present invention, including:
  • the edge extraction module 101 is configured to perform edge image extraction on the template image and the test image of the circuit board of the acquired electronic component to obtain a first edge image and a second edge image;
  • the edge extraction module 101 obtains the edge image information of the template image and the test image of the acquired electronic component, and obtains the edge detail information of the template image and the test image to ensure that the detection result of the subsequent step is more accurate.
  • the feature extraction module 102 is configured to separately partition the first edge image and the second edge image, and perform feature extraction on each partition of the first edge image and the second edge image to obtain a first feature parameter of the template image and Testing a second characteristic parameter of the image;
  • the feature extraction module 102 performs segmentation on the first edge image and the second edge image, and performs feature extraction on each partition of the first edge image and the second edge image to obtain a first feature parameter and a test of the template image.
  • the second characteristic parameter of the image is used to calculate the similarity value of the template image and the test image in the subsequent steps.
  • the similarity calculation module 103 is configured to calculate a similarity value between the template image and the test image according to the first feature parameter and the second feature parameter;
  • the similarity values of the two are calculated according to the first feature parameter including the template image feature information and the second feature parameter including the test image, and the electronic circuit to be tested is performed according to the similarity value for the subsequent step.
  • Component missing parts detection is performed according to the similarity value for the subsequent step.
  • the detecting module 104 is configured to perform electronic component missing component detection on the circuit board according to the similarity value.
  • the electronic component missing component detecting system performs edge image extraction on the template image and the test image of the obtained electronic component board by the edge extraction module 101 to obtain edge information of the template image and the test image; and the feature extraction module 102 Separating the first edge image and the second edge image separately, and performing feature extraction on each partition of the first edge image and the second edge image to obtain a first feature parameter of the template image and a second test image
  • the feature parameter; the reuse similarity calculation module 103 performs the electronic component missing component detection on the circuit board by using the detection module 104 according to the similarity value calculated by the first feature parameter and the second feature parameter.
  • the edge extraction module 101 may also For:
  • Edge image extraction is performed on the template image and the test image from three directions, respectively, to obtain a first edge image of the template image in three directions and a second edge image of the test image; wherein the three directions are respectively The horizontal direction, the vertical direction, and the diagonal direction of the template image and the test image.
  • the first edge image and the second image in the three directions of the template image and the test image are obtained by performing edge extraction on the horizontal direction, the vertical direction, and the diagonal direction of the template image and the test image, respectively.
  • Edge image is ensured that the obtained edge image contains more edge detail information, and the effectiveness of feature extraction on the obtained edge image in the subsequent steps is also ensured.
  • the edge extraction module 101 can also be used to perform horizontal, vertical, and opposite directions by the above-described formulas (1) to (3), respectively.
  • the edge direction extracts the edge of the template image and the test image.
  • the template image and the test image respectively obtain edge images in three directions, from the original one original image to the edge image in three directions.
  • FIG. 3 is a schematic structural diagram of a feature extraction module of an electronic component missing component detecting system according to another embodiment of the present invention, including:
  • the partitioning module 1021 is configured to divide the first edge image of the template image in three directions and the second edge image of the test image into a plurality of partitions, respectively.
  • the partitioning module 1021 can also be used to:
  • the first edge image of the template image in the three directions and the second edge image of the test image are respectively divided into four non-overlapping and identically sized partitions.
  • the obtained first edge image and the second edge image are respectively divided into four non-overlapping and identically-sized partitions, which effectively preserves the global structural features of the original template image and the test image, which is beneficial for subsequent
  • the accuracy of calculating the similarity values of the two steps further ensures the accuracy of the electronic component missing component detection.
  • the acquired edge image may also be divided into partitions of different sizes or overlapping each other, and the specific partitioning method may be adjusted according to actual detection accuracy requirements and other actual requirements.
  • the feature parameter calculation module 1022 is configured to calculate a local binary eigenvalue of each partition by using a local binary mode method, and combine the local binary eigenvalues of each partition of the first edge image and the second edge image in a vector manner respectively. Obtaining a first feature parameter of the template image and a second feature parameter of the test image.
  • the Local Binary Patterns (LBP) described in the above embodiments are non-parametric operators that describe the local spatial structure of an image. It not only accurately describes detailed texture information such as points, lines, edges, but also has The advantages of translation invariance and rotation invariance; but because the local binary pattern feature is represented by a histogram, the histogram is a feature representation with weak resolution, because it is a first-order statistical feature. Ignoring the global structural features of the image, if only one histogram is generated for the entire image, important structural difference information is inevitably lost. Therefore, the electronic component missing component detecting method of the present invention obtains the edge extraction module 101 by the partitioning module 1021.
  • the edge images in the three directions are each divided into a plurality of partitions. In this embodiment, each edge image is divided into four partitions of the same size and not overlapping.
  • the edge image in each direction can be equally divided into four partitions, such that the first edge image in three directions of the template image is divided into 12 partitions. Similarly, the second edge image in the three directions of the test image is also divided into 12 partitions.
  • the feature parameter calculating module 1022 respectively calculates the local binary value of the 12 partitions of the first edge image and the local binary features of the 12 partitions of the second edge image by using the local binary pattern method. And combining the local binary eigenvalues of the 12 partitions of the first edge image and the local binary eigenvalues of the 12 partitions of the second edge image in a vector manner to obtain the first characteristic parameter of the template image and The second characteristic parameter of the test image. That is, the first feature parameter is a composite vector composed of 12 local binary eigenvalues of the first edge image, and the second feature parameter is a composite vector composed of 12 local binary eigenvalues of the second edge image.
  • the electronic component missing component detecting system of the present invention is configured to:
  • the electronic component missing component detecting system of the present invention can also be used to:
  • H1 and H2 represent the characteristic histogram of the first characteristic parameter and the characteristic histogram of the second characteristic parameter, respectively, and B represents the interval length of the histogram.
  • the electronic component missing component detecting system of the present invention is further configured to:
  • the first edge image and the second edge image in the three directions of the template image and the test image are respectively divided into four partitions that are not overlapping and the same size.
  • the electronic component missing component detecting system of the present invention can also be used to:
  • the electronic component missing component detecting system of the present invention can also be used to:
  • the electronic component missing component detection is performed on the circuit board to be tested according to the similarity value between the calculated template image and the test image.
  • R represents the similarity value between the template image and the test image
  • T represents a preset threshold
  • the electronic component missing component detecting method and system respectively perform edge image extraction in three directions on a template image and a test image of a circuit board of the acquired electronic component, so that the obtained edge image contains more detailed information;
  • the first edge image and the second edge image are respectively partitioned, and feature extraction of the local binary pattern is performed on each partition of the first edge image and the second edge image, so that the obtained feature parameter includes more global structural features.

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Abstract

L'invention concerne un procédé et un système de contrôle de vérification d'un composant électronique manquant. Le procédé consiste: à effectuer une extraction de contour d'image, respectivement sur une image modèle acquise et une image test d'une carte de circuit imprimé comportant des composants électroniques, de manière à obtenir une première image de contour et une seconde image de contour (S101); à diviser en zones les première et seconde images de contour respectivement, et à extraire des caractéristiques des zones des première et seconde images de contour pour obtenir un premier paramètre de caractéristiques de l'image modèle et un second paramètre de caractéristiques de l'image test (S102); à calculer, selon le premier paramètre de caractéristiques et le second paramètre de caractéristiques, une valeur de similarité entre l'image modèle et l'image test (S103); et à effectuer, selon la valeur de similarité, un contrôle de vérification d'un composant électronique manquant sur la carte de circuit imprimé (S104). Le procédé et le système de l'invention augmentent efficacement la précision de contrôle de vérification d'un composant électronique manquant, assurant ainsi la qualité des cartes de circuit imprimé inspectées.
PCT/CN2016/112888 2016-04-20 2016-12-29 Procédé et système de contrôle de vérification d'un composant électronique manquant WO2017181724A1 (fr)

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