CN114862817A - Circuit board golden finger area defect detection method, system, device and medium - Google Patents

Circuit board golden finger area defect detection method, system, device and medium Download PDF

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Publication number
CN114862817A
CN114862817A CN202210568397.9A CN202210568397A CN114862817A CN 114862817 A CN114862817 A CN 114862817A CN 202210568397 A CN202210568397 A CN 202210568397A CN 114862817 A CN114862817 A CN 114862817A
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image
golden finger
finger area
area
circuit
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不公告发明人
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Chengdu Shuzhilian Technology Co Ltd
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Chengdu Shuzhilian Technology Co Ltd
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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
    • 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
    • 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
    • G01N21/956—Inspecting patterns on the surface of objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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
    • 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
    • G01N21/956—Inspecting patterns on the surface of objects
    • G01N2021/95638—Inspecting patterns on the surface of objects for PCB's
    • G—PHYSICS
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    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
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    • 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]
    • 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
    • G06T2207/30168—Image quality inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/07—Target detection

Abstract

The invention discloses a method, a system, a device and a medium for detecting golden finger area defects of a circuit board, which relate to the field of electronic component production.

Description

Circuit board golden finger area defect detection method, system, device and medium
Technical Field
The invention relates to the field of electronic component production, in particular to a method, a system, a device and a medium for detecting defects of a golden finger area of a circuit board.
Background
With the rapid development of the electronic industry, the circuit design becomes more complex and finer, and the requirements of the printed circuit board as the main carrier of the electronic product circuit on the manufacturing process are more and more strict. In the production process of the printed circuit board, a plurality of processes are involved, and different processes can cause different degrees of defects on the surface of the printed circuit board. The gold finger area is an area for transmitting signals, which is formed by a plurality of golden conductive contact pads on the printed circuit board, and is called a gold finger because the surface of the gold finger area is plated with gold and the conductive contact pads are arranged like fingers. In the printed circuit board, the gold finger is a critical area affecting the quality of the printed circuit board as an outlet of an external connection network. At present, for the detection work of the printed circuit board, pictures are generally shot through an AOI automatic optical detection machine, and then manual visual inspection is used for classifying defects, but the manual visual inspection has great subjectivity, and particularly for the detection of golden finger regions with similar characteristics, the visual inspection result can be influenced to a great extent under the long-time manual work, so that the detection result is inaccurate.
Disclosure of Invention
The invention provides a golden finger area defect detection method, a golden finger area defect detection system, a golden finger area defect detection device and a golden finger area defect detection medium, aiming at solving the problem that when the existing printed circuit board is detected, the golden finger area with similar characteristics can affect the visual detection result to a great extent under the long-time manual work, so that the obtained detection result is unstable and inaccurate.
In order to achieve the above object, the present invention provides a method for detecting defects in a golden finger area of a circuit board, comprising the following steps:
obtaining a circuit image to be detected;
identifying the circuit image to be detected, obtaining a target area coordinate, and segmenting the circuit image to be detected according to the target area coordinate to obtain a golden finger area image;
processing the golden finger area image to obtain image data;
and analyzing the image data to obtain a defect detection result.
The principle of the invention is as follows: after obtaining a circuit image to be detected, a computer identifies the circuit image to be detected, positions a golden finger image area in the circuit to be detected, obtains coordinates of the golden finger image area in the circuit image to be detected, divides the circuit image to be detected according to the coordinates to obtain a golden finger area image, analyzes the golden finger area image through a series of image processing algorithms to obtain image data, and finally analyzes the image data to obtain a defect detection result of the printed circuit board.
Further, in order to accurately position the golden finger region in the circuit, a target detection frame based on deep learning is adopted to achieve the acquisition of the coordinates of the target region, and the acquisition of the coordinates of the target region of the circuit to be detected comprises the following steps:
obtaining a plurality of circuit sample diagrams, and labeling the golden finger areas in the circuit sample diagrams to obtain a first training set;
training a first target detection framework based on the first training set to obtain a first target detection model;
and identifying the circuit image to be detected by using the first target detection model to obtain the target detection area coordinate.
Furthermore, if the gold finger area has a defect of copper residue, a short circuit may be caused when the gold finger area is used, so that the quality of the printed circuit board is not up to the standard, and the identification of the defect of copper residue in the gold finger area is realized by adopting a target detection framework based on deep learning, so that the method further comprises the following steps before processing the image of the gold finger area:
obtaining a plurality of circuit defect sample graphs, and marking the defect positions and types in the circuit defect sample graphs to obtain a second training set;
training a second target detection framework based on the second training set to obtain a second target detection model;
the step of processing the golden finger area image to obtain image data comprises the following steps:
and identifying the golden finger area image by using the second target detection model to obtain an image identification result.
The method comprises the steps of acquiring a target detection model, acquiring a training set, and identifying the target detection model by using a fast-RCNN target detection framework combined with an FPN structure, wherein the characteristics in the sample set used for training the target detection framework need to be manually labeled to obtain the training set, the required sample amount is as much as possible to cause larger workload, in order to reduce the workload, the fast-RCNN target detection framework combined with the FPN structure is adopted, the fast-RCNN target detection framework combined with the FPN structure is suitable for the deep learning problem of small sample amount, and the workload of processing the sample set in the early stage can be effectively reduced.
Further, if the golden finger area has a contamination defect, the circuit contact may be poor when the golden finger area is used, so that the quality of the printed circuit board does not reach the standard, and when the contamination defect of the golden finger area is identified, the processing of the image of the golden finger area includes the following steps:
in order to facilitate computer image processing, carrying out binarization processing on the golden finger area image to obtain a binarization image;
in order to filter out noise which may appear in an image and improve the accuracy of image processing, filtering the binary image to obtain a first image;
the gray value of the image pixel after binarization is 255 or 0, the pixel point of the dirty position existing in the golden finger area can be distinguished from the pixel point of the normal circuit image after binarization processing, the pixel point and the printed circuit board substrate area are presented as black together, the normal circuit part is presented as white, therefore, the dirt existing in the golden finger area can be realized by detecting the area of the communication area in the image, and therefore, the communication area in the first image is detected;
calculating the area of each connected region in the first image;
the analyzing the image data to obtain a defect detection result comprises the following steps:
because the golden finger area may have dirt, and the area of the normal partial area is reduced due to the dirt, the size of the area of the connected area is judged, and if the area of the connected area is smaller than a standard value, the golden finger area image has defects.
In order to avoid missing detection caused by the fact that small dirt in the golden finger area image is removed through filtering processing, the filtering processing is achieved through a morphological opening operation method, the binary image is subjected to corrosion processing firstly, then the image subjected to expansion processing is subjected to expansion processing, the first image is obtained, white dots, burrs and bridges which are isolated outside the image can be eliminated through the morphological opening operation, and the total position and the shape of the image are enabled to be unchanged.
Further, if the golden finger area has a metal oxidation defect, the service life of the printed circuit board may be reduced, so that the quality of the printed circuit board does not reach the standard, and when the golden finger area metal oxidation defect is identified, the processing of the golden finger area image includes the following steps:
in order to facilitate computer image processing, carrying out gray processing on the golden finger area image to obtain a gray image;
in order to filter out noise which may appear in an image and improve the accuracy of image processing, filtering the gray image to obtain a second image;
the detection of the printed circuit board is mostly carried out in an industrial production environment, an industrial light source adopted when an image is obtained is relatively stable, when a metal oxidation defect exists in a golden finger, the color of the image in the golden finger area is darker than that of a standard golden finger area, and the color of a substrate of the printed circuit board is kept unchanged, so that the gray level average value of the oxidized image is reduced, whether the oxidation defect exists in the golden finger area of the printed circuit board can be judged according to the gray level average value of the image in the golden finger area, and therefore, the gray level average value of the second image is calculated to obtain second image data;
the analyzing the image data to obtain a defect detection result comprises the following steps:
and comparing the second image data with the standard image gray scale interval, wherein if the second image data is not in the standard image gray scale interval, the golden finger area image has defects.
When a metal oxidation defect exists in the golden finger area, the golden area originally becomes dark golden, the collected image is an RGB image, the golden color and the dark golden color mainly have difference on an R channel component and a G channel component of the image for the RGB image, and in order to avoid the influence of a B channel component on the image gray average value, the B channel component of the image can be kept fixed by compensating the B channel component of the image, so that a circuit to be tested is photographed under blue light to obtain the circuit to be tested image.
The acquired image is an RGB image, and for the RGB image, gold and dark gold mainly have difference on an R channel component and a G channel component of the image, so that in order to avoid influence of a B channel component on the image gray average value, the B channel component of the image can be excluded when the image gray average value is calculated, and the step of carrying out gray processing on the golden finger area image comprises the following steps:
obtaining an R component and a G component of the golden finger area image pixel;
performing weighted calculation on the R component and the G component to obtain a pixel gray value;
and generating a gray image according to the pixel gray value.
In order to achieve the above object, the present invention further provides a system for detecting defects in a golden finger area of a circuit board, the system comprising:
the image obtaining unit is used for obtaining an image of the circuit to be tested;
the area identification unit is used for identifying the circuit image to be detected, obtaining a target area coordinate, and segmenting the circuit image to be detected according to the target area coordinate to obtain a golden finger area image;
the processing unit is used for processing the golden finger area image to obtain image data;
the analysis unit is used for analyzing the image data to obtain a defect detection result;
the system is used for realizing the steps of the circuit board golden finger area defect detection method.
In order to achieve the above object, the present invention further provides a circuit board golden finger area defect detecting apparatus, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the circuit board golden finger area defect detecting method when executing the computer program.
In order to achieve the above object, the present invention further provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the circuit board golden finger area defect detection method.
One or more technical schemes provided by the invention at least have the following technical effects or advantages: according to the invention, through positioning the golden finger area on the printed circuit board and analyzing the key area of the image obtained after positioning, the defects of the golden finger area of the printed circuit board can be automatically detected through a computer, the problem that the detection result obtained by manually and visually detecting the golden finger area with similar characteristics is unstable and inaccurate is avoided, and the method has strong practicability.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention;
FIG. 1 is a schematic view of a gold finger area defect detection process according to the present invention;
FIG. 2 is a schematic diagram of a detection result of a golden finger region target in the present invention;
FIG. 3 is a schematic diagram illustrating a defect detection result of the remaining copper in the golden finger region according to the present invention;
FIG. 4 is a schematic diagram of a golden finger area defect detection system according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention, taken in conjunction with the accompanying drawings and detailed description, is set forth below. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflicting with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as specifically described and thus the scope of the present invention is not limited by the specific embodiments disclosed below.
Example one
Referring to fig. 1, the present invention provides a method for detecting a defect in a golden finger area of a circuit board, comprising the following steps:
obtaining a circuit image to be detected;
identifying the circuit image to be detected, obtaining a target area coordinate, and segmenting the circuit image to be detected according to the target area coordinate to obtain a golden finger area image;
processing the golden finger area image to obtain image data;
and analyzing the image data to obtain a defect detection result.
In an embodiment, the obtaining of the target area coordinate is implemented by a target detection framework based on deep learning, and the obtaining of the target area coordinate of the circuit to be tested includes the following steps:
obtaining a plurality of circuit sample diagrams, and labeling the golden finger areas in the circuit sample diagrams to obtain a first training set;
training a first target detection framework based on the first training set to obtain a first target detection model;
and identifying the circuit image to be detected by using the first target detection model to obtain the target detection area coordinate.
The target detection framework can be an RCNN (sequence-coupled neural network) series framework, a YOLO series framework and an SSD (solid State disk) series framework, the RCNN series target detection framework comprises an RCNN, Fast-RCNN framework and a Fast-RCNN framework, the Fast-RCNN target detection framework carries out four steps required by target detection, namely the four steps of candidate region generation, feature extraction, classifier classification and regressive of a regressor, and the four steps are all operated on a GPU (graphics processing unit), so that the operation efficiency is greatly improved, therefore, the Fast-RCNN target detection framework is preferably selected in the embodiment, and the FPN structure is combined, so that the target detection problem of a small sample size can be effectively applied.
The processing of the golden finger region image includes performing graying processing, binarization processing, filtering processing, and the like on the image, and a specific processing mode of the processing is determined according to a possible defect type of the golden finger region image, which is not limited in this embodiment.
Example two
Referring to fig. 1, the present invention provides a method for detecting defects in a gold finger area of a circuit board, which, on the basis of the first embodiment, further includes the following steps before processing an image of the gold finger area for detecting copper remaining defects in the gold finger area of the printed circuit board:
obtaining a plurality of circuit defect sample graphs, and marking the defect positions and types in the circuit defect sample graphs to obtain a second training set;
training a second target detection framework based on the second training set to obtain a second target detection model;
the step of processing the golden finger area image to obtain image data comprises the following steps:
and identifying the golden finger area image by using the second target detection model to obtain an image identification result, wherein the image identification result is shown in fig. 3.
The target detection framework can be an RCNN (sequence-coupled neural network) series framework, a YOLO series framework and an SSD (solid State disk) series framework, the RCNN series target detection framework comprises an RCNN, Fast-RCNN framework and a Fast-RCNN framework, the Fast-RCNN target detection framework carries out four steps required by target detection, namely the four steps of candidate region generation, feature extraction, classifier classification and regressive of a regressor, and the four steps are all operated on a GPU (graphics processing unit), so that the operation efficiency is greatly improved, therefore, the Fast-RCNN target detection framework is preferably selected in the embodiment, and the FPN structure is combined, so that the target detection problem of a small sample size can be effectively applied.
EXAMPLE III
Referring to fig. 1, the present invention provides a method for detecting a defect in a gold finger area of a printed circuit board, wherein for a smudge defect in the gold finger area of the printed circuit board, the processing of the gold finger area image includes the following steps:
carrying out binarization processing on the golden finger area image to obtain a binarization image;
filtering the binary image to obtain a first image;
detecting a connected region in the first image;
calculating the area of each connected region in the first image;
the analyzing the image data to obtain a defect detection result comprises the following steps:
and judging the size of the area of the connected region, and if the area of the connected region is smaller than a standard value, determining that the golden finger region image has defects.
The standard value of the area of the connected region is determined according to the design layout of the circuit to be tested in the actual detection, which is not limited herein.
The binarization processing is to find a proper gray threshold, set the gray value of all pixels in the image to be 0 or 255 according to the threshold, and the threshold selection method comprises a double-peak method, a P parameter method, a maximum inter-class variance method and the like.
The detection of the connected region and the calculation of the area of the connected region may be implemented by a Two-Pass algorithm or a Seed-Filling algorithm, which is not limited herein.
The filtering processing method comprises mean filtering, frame filtering, Gaussian filtering, morphology opening operation, morphology closing operation and the like, wherein the morphology opening operation is used for carrying out corrosion operation on the binary image and then carrying out expansion operation on the image after the corrosion operation to realize filtering processing, so that dots, burrs and small bridges in the image can be effectively removed, the overall shape of the image is not changed, and the morphology opening operation is preferably used for carrying out filtering processing on the image.
Example four
Referring to fig. 1, the present invention provides a method for detecting defects in a gold finger area of a printed circuit board, where, on the basis of the first embodiment, the processing of the gold finger area image includes the following steps:
carrying out gray processing on the golden finger area image to obtain a gray image;
filtering the gray level image to obtain a second image;
calculating the gray average value of the second image to obtain second image data;
the analyzing the image data to obtain a defect detection result comprises the following steps:
and comparing the second image data with the standard image gray scale interval, wherein if the second image data is not in the standard image gray scale interval, the golden finger area image has defects.
The standard image gray scale interval is determined by the specific requirement in actual detection, and this embodiment is not limited herein.
The filtering processing method comprises mean filtering, frame filtering, Gaussian filtering, morphology opening operation, morphology closing operation and the like, wherein the morphology opening operation is used for carrying out corrosion operation on the binary image and then carrying out expansion operation on the image after the corrosion operation to realize filtering processing, so that dots, burrs and small bridges in the image can be effectively removed, the overall shape of the image is not changed, and the morphology opening operation is preferably used for carrying out filtering processing on the image.
The graying processing includes a component method, a maximum value method, and a weighted average method, the weighted average method weights R, G, B three components of the color RGB image by different weights to calculate the gray value of each pixel, and preferably, the weighted average method performs graying processing on the gold finger region image, where the weights are determined according to the characteristics of R, G, B three components of the actual image, and this embodiment is not limited herein.
Furthermore, when the golden finger area has a metal oxidation defect, the golden area originally becomes dark golden, and the acquired image is an RGB image, so that the golden and dark golden mainly have difference on an R channel component and a G channel component of the image for the RGB image, and in order to avoid the influence of the B channel component on the image gray average value, the B channel component of the image can be compensated, so that the circuit to be detected is photographed under blue light to obtain the circuit to be detected image.
Further, in order to avoid that a B channel component affects the image grayscale average value, the B channel component of the image may be excluded when performing a graying process on the golden finger region image, where the graying process on the golden finger region image includes the following steps:
obtaining an R component and a G component of the golden finger area image pixel;
performing weighted calculation on the R component and the G component to obtain a pixel gray value;
and generating a gray image according to the pixel gray value.
EXAMPLE five
The fifth embodiment of the invention provides a device for detecting the golden finger area defects of a circuit board, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the step of detecting the golden finger area defects of the circuit board is realized when the processor executes the computer program.
EXAMPLE six
The sixth embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the method for detecting a gold finger area defect of a circuit board are implemented.
The processor may be a Central Processing Unit (CPU), other general purpose processor, a digital signal processor (digital signal processor), an Application Specific Integrated Circuit (Application Specific Integrated Circuit), an off-the-shelf programmable gate array (Field programmable gate array) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the circuit board golden finger area defect detection device in the invention by operating or executing the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
The circuit board golden finger area defect detection device can be stored in a computer readable storage medium if the circuit board golden finger area defect detection device is realized in the form of a software functional unit and is sold or used as an independent product. Based on such understanding, all or part of the flow in the method of the embodiments of the present invention may also be stored in a computer readable storage medium through a computer program, and when the computer program is executed by a processor, the computer program may implement the steps of the embodiments of the methods. Wherein the computer program comprises computer program code, an object code form, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying said computer program code, a recording medium, a usb-disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, a point carrier signal, a telecommunications signal, a software distribution medium, etc. It should be noted that the computer readable medium may contain content that is appropriately increased or decreased as required by legislation and patent practice in the jurisdiction.
While the invention has been described with respect to the basic concepts, it will be apparent to those skilled in the art that the foregoing detailed disclosure is only by way of example and not intended to limit the invention. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (12)

1. A method for detecting defects of a golden finger area of a circuit board is characterized by comprising the following steps:
obtaining a circuit image to be detected;
identifying the circuit image to be detected, obtaining a target area coordinate, and segmenting the circuit image to be detected according to the target area coordinate to obtain a golden finger area image;
processing the golden finger area image to obtain image data;
and analyzing the image data to obtain a defect detection result.
2. The method of claim 1, wherein the target area coordinates are obtained by:
obtaining a plurality of circuit sample diagrams, and labeling the golden finger areas in the circuit sample diagrams to obtain a first training set;
training a first target detection framework based on the first training set to obtain a first target detection model;
and identifying the circuit image to be detected by using the first target detection model to obtain the target area coordinate.
3. The method of claim 1, wherein the step of processing the golden finger area image further comprises the steps of:
obtaining a plurality of circuit defect sample graphs, and marking the defect positions and types in the circuit defect sample graphs to obtain a second training set;
training a second target detection framework based on the second training set to obtain a second target detection model;
the step of processing the golden finger area image to obtain image data comprises the following steps:
and identifying the golden finger area image by using the second target detection model to obtain an image identification result.
4. The method of claim 1, wherein the processing the golden finger area image comprises the following steps:
carrying out binarization processing on the golden finger area image to obtain a binarization image;
filtering the binary image to obtain a first image;
detecting a connected region in the first image;
calculating the area of each connected region in the first image;
the analyzing the image data to obtain a defect detection result comprises the following steps:
and judging the size of the area of the connected region, and if the area of the connected region is smaller than a standard value, determining that the golden finger region image has defects.
5. The method as claimed in claim 4, wherein the filtering process is a morphological opening operation on the binarized image.
6. The method of claim 1, wherein the processing the golden finger area image comprises the following steps:
carrying out gray processing on the golden finger area image to obtain a gray image;
filtering the gray level image to obtain a second image;
calculating the gray average value of the second image to obtain second image data;
the analyzing the image data to obtain a defect detection result comprises the following steps:
and comparing the second image data with the standard image gray scale interval, wherein if the second image data is not in the standard image gray scale interval, the golden finger area image has defects.
7. The method as claimed in claim 6, wherein the circuit under test is photographed under blue light to obtain the image of the circuit under test.
8. The method as claimed in claim 6, wherein the golden finger area image is an RGB image, and the graying process of the golden finger area image comprises the following steps:
obtaining an R component and a G component of the golden finger area image pixel;
performing weighted calculation on the R component and the G component to obtain a pixel gray value;
and generating a gray image according to the pixel gray value.
9. The method as claimed in claim 2 or 3, wherein the target detection framework is a fast-RCNN target detection framework combined with FPN structure.
10. A circuit board golden finger area defect detection system, the system comprising:
the image obtaining unit is used for obtaining an image of the circuit to be tested;
the area identification unit is used for identifying the circuit image to be detected, obtaining a target area coordinate, and segmenting the circuit image to be detected according to the target area coordinate to obtain a golden finger area image;
the processing unit is used for processing the golden finger area image to obtain image data;
and the analysis unit is used for analyzing the image data to obtain a defect detection result.
11. A circuit board golden finger area defect detecting device, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements the steps of the circuit board golden finger area defect detecting method according to any one of claims 1 to 8 when executing the computer program.
12. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for detecting defects in a gold finger area of a circuit board according to any one of claims 1 to 8.
CN202210568397.9A 2022-05-24 2022-05-24 Circuit board golden finger area defect detection method, system, device and medium Pending CN114862817A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115063618A (en) * 2022-08-17 2022-09-16 成都数之联科技股份有限公司 Defect positioning method, system, equipment and medium based on template matching
CN115063421A (en) * 2022-08-16 2022-09-16 成都数联云算科技有限公司 Pole piece region detection method, system and device, medium and defect detection method
CN116664566A (en) * 2023-07-28 2023-08-29 成都数智创新精益科技有限公司 OLED panel screen printing quality control method, system and device and medium

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115063421A (en) * 2022-08-16 2022-09-16 成都数联云算科技有限公司 Pole piece region detection method, system and device, medium and defect detection method
CN115063618A (en) * 2022-08-17 2022-09-16 成都数之联科技股份有限公司 Defect positioning method, system, equipment and medium based on template matching
CN115063618B (en) * 2022-08-17 2022-11-11 成都数之联科技股份有限公司 Defect positioning method, system, equipment and medium based on template matching
CN116664566A (en) * 2023-07-28 2023-08-29 成都数智创新精益科技有限公司 OLED panel screen printing quality control method, system and device and medium
CN116664566B (en) * 2023-07-28 2023-09-26 成都数智创新精益科技有限公司 OLED panel screen printing quality control method, system and device and medium

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