WO2017088553A1 - Procédé et système pour identifier rapidement et marquer une direction de polarité de composant électronique - Google Patents

Procédé et système pour identifier rapidement et marquer une direction de polarité de composant électronique Download PDF

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WO2017088553A1
WO2017088553A1 PCT/CN2016/098228 CN2016098228W WO2017088553A1 WO 2017088553 A1 WO2017088553 A1 WO 2017088553A1 CN 2016098228 W CN2016098228 W CN 2016098228W WO 2017088553 A1 WO2017088553 A1 WO 2017088553A1
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electronic component
image
polarity direction
electronic components
target electronic
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PCT/CN2016/098228
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English (en)
Chinese (zh)
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杨铭
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广州视源电子科技股份有限公司
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    • 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
    • G06T7/001—Industrial image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • 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 invention relates to the field of automatic optical detection, and in particular to a method and a system for quickly identifying and marking the polarity direction of an electronic component.
  • a method for quickly identifying the polarity direction of an electronic component includes the following steps:
  • the image data of the target electronic component is forwardly calculated, and the classification features of the polarity direction category of the target electronic component are obtained, and the target electronic components are obtained according to the classification features. Probability distribution of sexual direction categories;
  • the polarity direction category with the highest probability is selected as the polarity direction category of the target electronic component.
  • the image is obtained by including the target electronic component, and then subjected to the forward calculation by using the trained convolutional neural network to obtain the classification feature of the polarity direction category of the target electronic component, and then the target electronic component is obtained.
  • Probability distribution of various polar direction categories belonging to various electronic components The polarity direction category with the highest probability is selected as the polarity direction category of the target electronic component.
  • a convolutional neural network is used.
  • the convolutional neural network can automatically and accurately identify the polarity direction of electronic components, and it is not specific to the structure of electronic components. It is applicable to various electronic components with polarity to achieve cross- The polarity direction identification of the electronic components of the category is widely applicable.
  • the step of calculating the image data of the target electronic component by using the trained convolutional neural network to perform the forward calculation of the image data of the target electronic component includes the following steps:
  • the image data is convoluted by the convolution layer, then nonlinearly transformed by the activation function layer, and then pooled by the pooling layer, and then the classification feature of the polarity direction category of the target electronic component is obtained through the fully connected layer.
  • the convolutional neural network includes a convolution module and a fully connected layer, and the convolution module includes a convolution layer, an activation function layer, and a pooling layer that are sequentially connected.
  • the trained convolutional neural network is obtained by the following steps:
  • the step of establishing a sample set of various polarity directions of various types of electronic components includes the following steps:
  • the precise position of the components, the corresponding electronic components are adjusted according to the precise position of the corresponding electronic components, so that the corresponding electronic components are located at the center of the image of various electronic components, and image sample sets of various polar directions of various electronic components are obtained.
  • there are five convolutional modules wherein the number of convolution kernels of the five convolutional layers is 24, 64, 96, 96, and 64, respectively, and the convolution kernel sizes of the five convolutional layers are respectively 7 ⁇ 7, 5 ⁇ 5, 3 ⁇ 3, 3 ⁇ 3, 3 ⁇ 3, 5 convolutional layers have a step size of 1; the total connection layer is 2, and the number of hidden nodes of the 2 fully connected layers is 512 and 4, respectively.
  • a method for marking the polarity direction of an electronic component includes the following steps:
  • the polarity direction information of the target electronic component is marked in the panel file, and the panel file is used to store various attribute information of the electronic component.
  • the above-mentioned method for labeling the polarity direction of the electronic component can be applied to the AOI board production, and the polarity direction of the components on the PCB board is automatically and accurately marked, thereby improving the automation level of the panel production and improving the efficiency and accuracy of the panel production.
  • a rapid identification system for the polarity direction of electronic components comprising the following units:
  • the calculating unit is configured to perform forward calculation on the image data including the target electronic component by using the trained convolutional neural network, obtain a classification feature of the polarity direction category of the target electronic component, and acquire the target electronic component according to the classification feature to belong to various types of electronic Probability distribution of various polar direction categories of components;
  • the selection unit is configured to select the polarity direction category with the highest probability as the polarity direction category of the target electronic component.
  • the image is obtained by including the target electronic component, and then subjected to the forward calculation by using the trained convolutional neural network to obtain the classification feature of the polarity direction category of the target electronic component, and then the target electronic component is obtained.
  • the probability distribution of various polarity direction categories belonging to various electronic components is selected as the polarity direction category of the target electronic component.
  • a convolutional neural network is used, which can automatically and accurately identify the polarity direction of electronic components through a convolutional neural network, and is not specific to a specific electronic component structure, and is applicable to various electronic components with polarity. It realizes the polarity direction identification of electronic components across categories and has wide applicability.
  • An annotation system for the polarity direction of an electronic component comprising a labeling unit and a quick identification system for the polarity direction of the electronic component, wherein the labeling unit is used to mark the polarity direction information of the target electronic component in the panel file, and the panel file is used for Save various attribute information of electronic components.
  • the above-mentioned electronic component polarity direction labeling system can be applied to AOI board production, and the polarity direction of the components on the PCB board is automatically and accurately marked, thereby improving the automation level of the panel production and improving the efficiency and accuracy of the panel production.
  • FIG. 1 is a schematic flow chart of a method for quickly identifying a polarity direction of an electronic component in one embodiment
  • FIG. 2 is a schematic structural view of a convolution module in one embodiment
  • FIG. 3 is a schematic structural view of a convolutional neural network in one embodiment
  • FIG. 4 is a schematic structural view of a rapid identification system for the polarity direction of an electronic component in one embodiment
  • FIG. 5 is a schematic structural view of a rapid identification system for the polarity direction of an electronic component in one embodiment
  • FIG. 6 is a partial structural schematic view of a rapid identification system for the polarity direction of an electronic component in one embodiment
  • FIG. 7 is a schematic structural view of a rapid identification system for the polarity direction of an electronic component in one embodiment
  • FIG. 8 is a schematic structural view of a rapid identification system for the polarity direction of an electronic component in one embodiment
  • Figure 9 is a block diagram showing the structure of the polarity of the electronic component in one embodiment.
  • the method for quickly identifying the polarity direction of an electronic component of the present invention includes the following steps:
  • Step S101 Acquire an image including the target electronic component
  • the image containing the electronic component may be a PCB card image that needs to identify the polarity of the electronic component, or other images that need to identify the polarity of the electronic component.
  • Step S102 Perform forward calculation on the image data including the target electronic component by using the trained convolutional neural network to obtain a classification feature of the polarity direction category of the target electronic component, and obtain the target electronic component belonging to various electronic components according to the classification feature. Probability distribution of various polar direction categories;
  • the image data including the target electronic component is forwardly calculated by using the convolution module and the fully connected layer in the convolutional neural network after training, and the image data including the target electronic component is passed through the convolution module.
  • the fully connected layer can obtain the classification characteristics of the polarity direction category of the target electronic component;
  • the various polarity direction categories refer to various polarity directions of various electronic components, including various electronic components, and the probability distribution obtained can be obtained.
  • the trained convolutional neural network can operate on the image, identify the polarity direction of the target electronic components, and obtain the probability distribution.
  • Step S103 Select the polarity direction category with the highest probability as the polarity direction category of the target electronic component.
  • the above steps S101, S102 and S103 are processes for performing online testing using the trained convolutional neural network.
  • the step of calculating the image data of the target electronic component by using the trained convolutional neural network to perform the forward calculation of the image data of the target electronic component includes the following steps:
  • the image data is convoluted by the convolution layer, then nonlinearly transformed by the activation function layer, and then pooled by the pooling layer, and then the classification feature of the polarity direction category of the target electronic component is obtained through the fully connected layer.
  • the convolutional neural network includes a convolution module and a fully connected layer, and the convolution module includes a convolution layer, an activation function layer, and a pooling layer that are sequentially connected.
  • the activation function layer may be a ReLU function layer.
  • the convolution module includes a convolution layer, a ReLU function layer, and a pooling layer that are sequentially connected, and convolves the image data of the target electronic component through the convolution layer, and then performs nonlinear transformation through the ReLU function layer. Then, the pooling operation is performed through the pooling layer, and then the classification feature of the polarity direction category of the target electronic component is obtained through the fully connected layer.
  • the ReLU function in the convolutional module of the convolutional neural network is an activation function that can be used for nonlinear transformation;
  • the pooling layer is used for pooling operations, and can aggregate statistics on features of different positions of image samples, and This will cause the final result to be over-fitting.
  • the convolutional neural network has a very strong expression ability, which can effectively solve the problem of low precision in multi-classification tasks. Even in the cross-class electronic component polarity direction recognition task, a very high accuracy can be achieved.
  • the trained convolutional neural network is obtained by the following steps:
  • the feature training convolutional neural network enables the convolutional neural network to identify various polar directions of various electronic components.
  • the use of external data sets to train convolutional neural networks can enhance the identification of convolutional neural networks, but High hardware computing power requirements, usually require high-end GPUs to meet the actual computing speed requirements, thus increasing hardware costs.
  • the technical solution of the invention does not need to use the external data set to train the convolutional neural network, and can accurately and quickly determine the automatic polarity direction of the electronic component under the low-cost hardware configuration, and is suitable for various electronic components (such as capacitors and sockets). , resistance, etc.).
  • the main feature of the scheme is that it does not depend on special hardware mechanisms, and the cost is low, which greatly reduces the requirements of the algorithm for hardware computing power and storage space, and solves the problem of high cost of high-precision technical solutions.
  • the convolutional neural network can be offlinely trained according to the image sample sets of various polarity directions of various electronic components, so that the convolutional neural network can recognize the polarity direction information of the electronic components in the image, thereby being online
  • the trained convolutional neural network is used in the test to identify the polarity direction of the target electronic component.
  • the step of establishing a sample set of various polarity directions of various types of electronic components includes the following steps:
  • the precise position of the components, the corresponding electronic components are adjusted according to the precise position of the corresponding electronic components, so that the corresponding electronic components are located at the center of the image of various electronic components, and image sample sets of various polar directions of various electronic components are obtained.
  • the image sample sets obtained by the above methods are directly taken from the PCB card image, and are images of actual electronic components, which are more instructive.
  • the camera can be set up on the PCB board production line, and different types of PCBs can be collected in batches. Card image, and board tracking technology to avoid repeating a certain PCB board, so each type of PCB board contains multiple image samples, each image sample corresponds to a certain type of PCB board; The corresponding PCB template map needs to be saved when collecting the PCB board image;
  • the position of the PCB card image in multiple image samples may be offset. It is necessary to use the corresponding template PCB template map as a reference, perform image registration for each image sample, and use the electronic component position information of the model PCB card (from The panel file or manual labeling automatically intercepts the electronic component image and automatically labels the electronic component image according to the electronic component category information (from the panel file or manual labeling);
  • the precise position of the electronic components in the image sample is further obtained by matching the electronic components in the PCB template image, and the electronic component images are aligned and adjusted to ensure that the electronic components are located at the center of the image, and image samples of various polar directions of the electronic components are obtained. set.
  • FIG. 3 there are five convolutional modules, wherein the number of convolution kernels of the five convolutional layers is 24, 64, 96, 96, and 64, respectively, and 5 convolutional layers.
  • the size of the convolution kernel is 7 ⁇ 7, 5 ⁇ 5, 3 ⁇ 3, 3 ⁇ 3, 3 ⁇ 3, and the steps of the five convolution layers are all 1; the total connection layer is 2, of which 2 are all.
  • the number of hidden nodes in the connection layer is 512 and 4, respectively.
  • the rapid identification of the polarity direction of the electronic component can be better achieved.
  • the target electronic components in the image are adjusted by the template image of the target electronic component, so that the trained convolutional neural network can more easily identify the target electronic component.
  • the following steps are further included:
  • the above steps are a process of detecting the polarity direction, and it is possible to judge whether or not the electronic component is correctly mounted.
  • the image of the target electronic component acquired by the online test is a PCB card image
  • the corresponding PCB template is matched and matched to obtain the precise position of the target electronic component, and the target electronic component in the image is aligned and adjusted. To ensure that the target electronic component is at the center of the image.
  • the corresponding PCB template is used for the matching, and the PCB template may be a PCB template corresponding to the image sample set image, or may be a PCB template different from the PCB template corresponding to the image sample set image, if In the former, the PCB template corresponding to the image sample set image has been saved during offline training, and can be directly used. In the latter case, in addition to obtaining the PCB board image, the online PCB template needs to obtain the corresponding PCB template from the outside to achieve the subsequent matching match.
  • the implementation of the solution can identify the polarity direction of all electronic components in the acquired PCB card image.
  • the steps of establishing a sample set of various polarity directions of various electronic components and the step of acquiring an image containing the target electronic component include the following steps:
  • the size of the adjusted image is normalized.
  • normalizing the size of the adjusted image may facilitate training of the convolutional neural network to make the convolutional neural network image sample The learning of multi-layer features is more accurate.
  • the normalized processing of the size of the adjusted image facilitates the forward calculation processing of the image of the target electronic component by the convolutional neural network, and accelerates the recognition process of the electronic component.
  • the method for quickly identifying the polarity direction of the electronic component of the present invention uses a convolutional neural network to automatically and accurately identify the polarity direction of the electronic component, and is not specific to a specific electronic component structure, and is applicable to various polarities.
  • the electronic component as long as the image sample set for training the convolutional neural network covers a plurality of types of electronic components, the polarity direction identification of the electronic components across the categories can be realized, and the applicability is wide.
  • Running on the current mainstream GPU can also judge the regular PCB board (including about 20-30 components with polarity), and judge the average calculation time of a component on the GPU even within 1 millisecond. Meet a variety of real-time judgment scenarios; whether it is offline training or online testing, minimize manual intervention, and does not rely on special hardware mechanisms, the cost is lower; no need Powerful computing resources can even run on inexpensive embedded platforms in some scenarios.
  • the invention also provides a method for marking the polarity direction of an electronic component, comprising the following steps:
  • the polarity direction information of the target electronic component is marked in the panel file, and the panel file is used to store various attribute information of the electronic component.
  • the above-mentioned method for labeling the polarity direction of the electronic component can be applied to the AOI board production, and the polarity direction of the components on the PCB board is automatically and accurately marked, thereby improving the automation level of the panel production and improving the efficiency and accuracy of the panel production.
  • the present invention also provides a rapid identification system for the polarity direction of the electronic component.
  • a rapid identification system for the polarity direction of the electronic component of the present invention will be described in detail.
  • the fast identification system of the polarity direction of the electronic component in this embodiment includes the acquisition unit 210, the calculation unit 220 and the selection unit 230 in FIG. 4;
  • An acquiring unit 210 configured to acquire an image that includes the target electronic component
  • the calculating unit 220 is configured to perform forward calculation on the image data including the target electronic component by using the trained convolutional neural network, obtain a classification feature of the polarity direction category of the target electronic component, and obtain the target electronic component according to the classification feature. Probability distribution of various polar direction categories of electronic components;
  • the selecting unit 230 is configured to select a polarity direction category with the highest probability as the polarity direction category of the target electronic component.
  • the image is obtained by including the target electronic component, and then subjected to the forward calculation by using the trained convolutional neural network to obtain the classification feature of the polarity direction category of the target electronic component, and then the target electronic component is obtained.
  • the probability distribution of various polarity direction categories belonging to various electronic components is selected as the polarity direction category of the target electronic component.
  • This scheme uses a convolutional neural network to automatically and accurately identify the poles of electronic components through convolutional neural networks. Sexual orientation, and is not specific to the structure of electronic components. It is applicable to various electronic components with polarity, and realizes the polarity direction identification of electronic components across categories, and has wide applicability.
  • the calculation unit 220 performs convolution operation on the image data through the convolution layer, then performs nonlinear transformation through the activation function layer, performs pooling operation through the pooling layer, and then obtains target electrons through the fully connected layer.
  • a classification feature of a polarity direction category of the component wherein the convolutional neural network includes a convolution module and a fully connected layer, and the convolution module includes a convolution layer, an activation function layer, and a pooling layer that are sequentially connected.
  • the rapid identification system of the polarity direction of the electronic component further includes an establishing unit 240 and a training unit 250;
  • the establishing unit 240 is configured to establish an image sample set of various polarity directions of various electronic components
  • the training unit 250 is configured to perform forward calculation on each image sample data of the image sample set by using the convolution module and the fully connected layer in the convolutional neural network, and obtain classifications of various polarity direction categories of various electronic components.
  • the feature trains the convolutional neural network according to each classification feature, so that the convolutional neural network can recognize various polar directions of various electronic components.
  • the establishing unit 240 includes a registration unit 241 and an intercept processing unit 242;
  • the registration unit 241 is configured to acquire a PCB card image and a PCB template image, and perform position registration on the PCB card image by using the PCB template image as a reference;
  • the intercepting processing unit 242 is configured to intercept the electronic component image on the PCB card image after the position registration, and match various electronic components in the electronic component images by various electronic components in the PCB template image to obtain various electronic components.
  • the precise position of the corresponding electronic components in the image, the corresponding electronic components are adjusted according to the precise position of the corresponding electronic components, so that the corresponding electronic components are located at the center of the image of various electronic components, and image samples of various polar directions of various electronic components are obtained. set.
  • there are five convolutional modules wherein the number of convolution kernels of the five convolutional layers is 24, 64, 96, 96, and 64, respectively, and the convolution kernel sizes of the five convolutional layers are respectively 7 ⁇ 7, 5 ⁇ 5, 3 ⁇ 3, 3 ⁇ 3, 3 ⁇ 3, the steps of the five convolutional layers are all 1; the total connection layer is 2, wherein the number of hidden nodes of the 2 fully connected layers They are 512 and 4 respectively.
  • the rapid identification system of the polarity direction of the electronic component is further Include a pre-processing unit 260;
  • the pre-processing unit 260 is configured to match the target electronic component in the image with the template image of the target electronic component after acquiring the image of the target electronic component, to obtain the precise position of the target electronic component in the image, according to the precise position
  • the target electronic components in the image are adjusted such that the target electronic components in the image are located at the center of the image, and the adjusted images are used for forward calculation by the trained convolutional neural network.
  • the fast identification system of the polarity direction of the electronic component further includes an alarm unit 270 for selecting the polarity direction category with the highest probability and the preset polarity direction of the electronic component. Different, the error alarm information is given.
  • the rapid identification system of the polarity direction of the electronic component of the present invention corresponds to the rapid identification method of the polarity direction of the electronic component of the present invention, and the technical features described in the embodiment of the rapid identification method of the polarity direction of the electronic component are beneficial.
  • the effects are all applicable to embodiments of the fast identification system for the polarity direction of the electronic components.
  • the present invention also provides an annotation system for the polarity direction of an electronic component, as shown in FIG. 9, comprising an identification unit 300 and the above-described identification system for the polarity direction of the electronic component, and the labeling unit 300 is configured to be based on the polarity direction of the electronic component.
  • the polar identification direction of the target electronic component determined by the system is quickly identified, the polarity direction information of the electronic component is marked in the panel file, and the panel file is used to store various attribute information of the electronic component.
  • the above-mentioned electronic component polarity direction labeling system can be applied to AOI board production, and the polarity direction of the components on the PCB board is automatically and accurately marked, thereby improving the automation level of the panel production and improving the efficiency and accuracy of the panel production.

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Abstract

L'invention concerne un procédé et un système pour identifier rapidement et marquer une direction de polarité de composant électronique. Le procédé consiste à : acquérir une image contenant un composant électronique cible (S101) ; réaliser un calcul depuis l'origine sur des données d'image du composant électronique cible par utilisation d'un réseau neuronal à convolution appris, acquérir une caractéristique de classification d'une catégorie de direction de polarité du composant électronique cible, et acquérir, selon la caractéristique de classification, une distribution de probabilité de catégories de direction de polarité du composant électronique cible selon tous les types de composants électroniques (S102) ; et sélectionner une catégorie de direction de polarité ayant une probabilité maximale comme catégorie de direction de polarité du composant électronique cible (S103). Dans le procédé, un réseau neuronal à convolution est utilisé, et, en conséquence, des directions de polarité de composants électroniques peuvent être identifiées automatiquement et de manière précise ; le procédé n'est pas dédié à une structure de composant électronique spécifiée mais est approprié pour tous les types de composants électroniques ayant une polarité, et, en conséquence, une identification de direction de polarité entre catégories de composants électroniques est mise en œuvre, et le procédé a une large applicabilité.
PCT/CN2016/098228 2015-11-23 2016-09-06 Procédé et système pour identifier rapidement et marquer une direction de polarité de composant électronique WO2017088553A1 (fr)

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