WO2021082921A1 - 电子设备背板外观瑕疵检测方法及设备 - Google Patents

电子设备背板外观瑕疵检测方法及设备 Download PDF

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Publication number
WO2021082921A1
WO2021082921A1 PCT/CN2020/120878 CN2020120878W WO2021082921A1 WO 2021082921 A1 WO2021082921 A1 WO 2021082921A1 CN 2020120878 W CN2020120878 W CN 2020120878W WO 2021082921 A1 WO2021082921 A1 WO 2021082921A1
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electronic device
backplane
image
defect
appearance
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PCT/CN2020/120878
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English (en)
French (fr)
Chinese (zh)
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徐鹏
沈圣远
常树林
姚巨虎
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上海悦易网络信息技术有限公司
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Priority to JP2022502080A priority Critical patent/JP2022539912A/ja
Publication of WO2021082921A1 publication Critical patent/WO2021082921A1/zh

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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
    • 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
    • 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]

Definitions

  • the invention relates to the field of computers, and in particular to a method and equipment for detecting defects in the appearance of an electronic device backplane.
  • the defect detection of the appearance of the back panel of second-hand electronic equipment such as mobile phones is mainly based on traditional image algorithms.
  • the detection is performed through color space conversion, filtering, feature point extraction, and pattern matching.
  • the traditional detection methods can only A defect is detected in a certain area, but the definition of the defect cannot be distinguished.
  • An object of the present invention is to provide a method and equipment for detecting the appearance defect of the backplane of an electronic device.
  • a method for detecting appearance defects of an electronic device backplane including:
  • the defect detection result of the backplane appearance area of the electronic device is received and output from the model of the FPN network combined with the backbone network.
  • the defect detection result includes: the type of the defect of the backplane of the electronic device, and the number of defects in the backplane of the electronic device Confidence of location and defect detection results.
  • extracting the appearance area image of the backplane of the electronic device from the appearance image of the electronic device includes:
  • the Unet instance segmentation method is used to extract the appearance area image of the backplane of the electronic device from the appearance image of the electronic device.
  • the first two layers of the backbone network adopt a res structure
  • the last two layers of the network adopt an inception structure
  • the method after receiving the output defect detection result of the backplane appearance area of the electronic device from the model of the FPN network combined with the backbone network, the method further includes:
  • output result information including the type of the defect of the backplane of the electronic device and the position of the defect on the backplane of the electronic device.
  • the method before inputting the backplane appearance area image into the FPN network combined with the backbone network model, the method further includes:
  • Step one preset the FPN network combined with the backbone network model and its initial model parameters
  • Step 2 Input the image of the backplane appearance area of the sample electronic device into the FPN network with the current model parameters combined with the backbone network model to obtain the defect prediction result of the backplane of the sample electronic device.
  • the defect prediction result includes: sample electronics The type of defect on the backplane of the device, the position of the defect in the backplane of the sample electronic device, and the confidence level of the defect detection result;
  • Step 3 Calculate the difference between the defect prediction result and the real defect result of the sample electronic device based on a preset objective function, and identify whether the difference is greater than a second preset threshold,
  • step 4 after updating the model parameters of the FPN network combined with the backbone network based on the difference, restart execution from step 2;
  • step 5 the FPN network combined with the backbone network model with the current model parameters is used as the model of the FPN network combined with the backbone network after the training.
  • adjusting the image of the appearance area of the backplane to an image with the same length and width includes:
  • the length direction of the image of the appearance area of the backplane is scaled.
  • a device for detecting the appearance defect of the backplane of an electronic device including:
  • the first device is used to obtain the appearance image of the electronic device
  • the second device is used to extract the appearance area image of the backboard of the electronic device from the appearance image of the electronic equipment, and adjust the appearance area image of the backboard to an image with the same length and width;
  • the third device is used to input the adjusted backplane appearance area image into the model of the FPN network combined with the backbone network after the training;
  • the fourth device is used to receive and output the defect detection result of the backplane appearance area of the electronic device from the model of the FPN network combined with the backbone network.
  • the defect detection result includes: the type of the defect of the backplane of the electronic device and the defect in the electronic device. The position in the backplane of the device and the confidence level of the defect detection result.
  • the second device is used to extract the appearance area image of the backboard of the electronic device from the appearance image of the electronic device by using Unet instance segmentation.
  • the first two layers of the backbone network adopt a res structure
  • the last two layers of the network adopt an inception structure
  • the fourth device is also used to identify whether the confidence of the defect detection result is greater than a first preset threshold, and if it is greater than the first preset threshold, the output includes the back of the electronic device.
  • the result information of the type of the board defect and the position of the defect in the backplane of the electronic device is also used to identify whether the confidence of the defect detection result is greater than a first preset threshold, and if it is greater than the first preset threshold, the output includes the back of the electronic device.
  • the above-mentioned equipment further includes a fifth device, including:
  • the fifth device is used to preset the FPN network combined with the backbone network model and its initial model parameters
  • the fifth and second device is used to input the image of the backplane appearance area of the sample electronic device into the FPN network with the current model parameters combined with the backbone network model to obtain the defect prediction result of the backplane of the sample electronic device, and the defect prediction result Including: the type of defect on the backplane of the sample electronic device, the position of the defect in the backplane of the sample electronic device, and the confidence level of the defect detection result;
  • the fifty-third device is configured to calculate the difference between the defect prediction result and the actual defect result of the sample electronic device based on a preset objective function, and to identify whether the difference is greater than a second preset threshold, and if the difference is Is greater than the second preset threshold, execute the fifth and fourth device, which is used to update the model parameters of the FPN network combined with the backbone network based on the difference based on the fifth and fourth device, and then restart from the fifth and second device carried out;
  • the fifth and fifth device is executed, and the model of the FPN network combined with the backbone network with the current model parameters is used as the model of the FPN network combined with the backbone network after the training is completed.
  • the second device is used to scale the longitudinal direction of the image of the appearance area of the backplane.
  • the present invention also provides a computing-based device, which includes:
  • a memory arranged to store computer-executable instructions which, when executed, cause the processor to:
  • the defect detection result of the backplane appearance area of the electronic device is received and output from the model of the FPN network combined with the backbone network.
  • the defect detection result includes: the type of the defect of the backplane of the electronic device and the number of defects in the backplane of the electronic device Confidence of location and defect detection results.
  • the present invention also provides a computer-readable storage medium on which computer-executable instructions are stored, wherein, when the computer-executable instructions are executed by a processor, the processor:
  • the defect detection result of the backplane appearance area of the electronic device is received and output from the model of the FPN network combined with the backbone network.
  • the defect detection result includes: the type of the defect of the backplane of the electronic device and the number of defects in the backplane of the electronic device Confidence of location and defect detection results.
  • the present invention obtains the appearance image of the electronic device; extracts the appearance area image of the backboard of the electronic device from the appearance image of the electronic device, and adjusts the appearance area image of the backboard to the length and width.
  • the same image input the adjusted backplane appearance area image into the model of the FPN network combined with the backbone network after training; receive the output defect detection result of the backplane appearance area of the electronic device from the model of the FPN network combined with the backbone network
  • the defect detection result includes: the type of the defect of the backplane of the electronic device, the position of the defect in the backplane of the electronic device, and the confidence level of the defect detection result, which can accurately identify the appearance of the backplane of the electronic device of the second-hand electronic device such as a mobile phone The difference in blemishes.
  • FIG. 1 shows a flowchart of a method for detecting appearance defects of an electronic device backplane according to an embodiment of the present invention
  • Fig. 2 shows a schematic diagram of a defect detection result according to an embodiment of the present invention
  • FIG. 3 shows a schematic diagram of a model of an FPN network combined with a backbone network according to an embodiment of the present invention.
  • the terminal, the equipment of the service network, and the trusted party all include one or more processors (CPU), input/output interfaces, network interfaces, and memory.
  • processors CPU
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • the memory may include non-permanent memory in a computer readable medium, random access memory (RAM) and/or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer readable media.
  • RAM random access memory
  • ROM read-only memory
  • flash RAM flash memory
  • Computer-readable media include permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology.
  • the information can be computer-readable instructions, data structures, program modules, or other data.
  • Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, Magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media can be used to store information that can be accessed by computing devices.
  • computer-readable media does not include non-transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.
  • the present invention provides a method for detecting appearance defects of an electronic device backplane, the method including:
  • Step S1 obtaining an appearance image of the electronic device
  • Step S2 extracting the appearance area image of the backplane of the electronic device from the appearance image of the electronic device, and adjusting the appearance area image of the backplane to an image with the same length and width;
  • the appearance area of the back panel of the electronic device includes the back area other than the front screen area and the side area where the electronics are installed.
  • the back area is generally equipped with components such as a camera.
  • the aspect ratio of the image of the appearance area of the backplane is abnormal, which is convenient for subsequent model recognition and avoids image loss.
  • the aspect ratio of the image of the appearance area of the backplane needs to be adjusted to 1:1; step S3, the adjusted appearance area of the backplane
  • Step S4 receiving and outputting the defect detection result of the backplane appearance area of the electronic device from the model of the FPN network combined with the backbone network.
  • the defect detection result includes: the defect type of the backplane of the electronic device and the defect on the back of the electronic device. The position in the board and the confidence level of the defect detection result.
  • the model of the FPN network combined with the backbone network can be shown in FIG. 3.
  • each defect detection result includes cls, x1, y1, x2, y2, score, where cls is the defect type, x1, y1, x2, y2 are the 4 coordinates of the position of the defect in the image of the backplane appearance area, and score is the confidence level of this defect.
  • the present invention mainly uses the improved feature pyramid (FPN) network combined with the deep learning model of the backbone network to accurately identify the difference in the appearance of the backplane of the electronic device of the second-hand electronic equipment such as the mobile phone, and can accurately distinguish the types of the defects.
  • FPN improved feature pyramid
  • step S2 extracting the appearance area image of the backplane of the electronic device from the appearance image of the electronic device includes:
  • the Unet instance segmentation method is used to extract the appearance area image of the backplane of the electronic device from the appearance image of the electronic device.
  • the appearance area image of the backplane can be obtained quickly and efficiently.
  • the first two layers of the backbone network adopt a res structure
  • the last two layers of the network adopt an inception structure
  • step S4 after receiving the output defect detection result of the appearance area of the backplane of the electronic device from the model of the FPN network combined with the backbone network, further includes:
  • output result information including the type of the defect of the backplane of the electronic device and the position of the defect on the backplane of the electronic device.
  • the types of defects on the back panel of the electronic device may include: cracks, bracket screen separation, deformation, missing chipping, large-area paint drop, small-area paint drop (deformation, depression to reveal color), depression and no change in color, deep scratches And the color is different from the surroundings, small dots and the color is different from the surroundings, chipping, etc.
  • step S3 before inputting the appearance area image of the backplane into the model of the FPN network combined with the backbone network, the method further includes:
  • Step one preset the FPN network combined with the backbone network model and its initial model parameters
  • Step 2 Input the image of the backplane appearance area of the sample electronic device into the FPN network with the current model parameters combined with the backbone network model to obtain the defect prediction result of the backplane of the sample electronic device.
  • the defect prediction result includes: sample electronics The type of defect on the backplane of the device, the position of the defect in the backplane of the sample electronic device, and the confidence level of the defect detection result;
  • Step 3 Calculate the difference between the defect prediction result and the real defect result of the sample electronic device based on a preset objective function, and identify whether the difference is greater than a second preset threshold,
  • step 4 after updating the model parameters of the FPN network combined with the backbone network based on the difference, restart execution from step 2;
  • step 5 the FPN network combined with the backbone network model with the current model parameters is used as the model of the FPN network combined with the backbone network after the training.
  • the model of the FPN network combined with the backbone network is cyclically trained to obtain a reliable model.
  • adjusting the appearance area image of the backplane to an image with the same length and width includes:
  • the image of the appearance area of the backplane may be scaled to a size of 2048*2048 pixels, so as to obtain the image of the appearance area of the backplane adjusted to an image with the same length and width.
  • the present invention provides a device for detecting defects in the appearance of a backplane of an electronic device, and the device includes:
  • the first device is used to obtain the appearance image of the electronic device
  • the second device is used to extract the appearance area image of the backboard of the electronic device from the appearance image of the electronic equipment, and adjust the appearance area image of the backboard to an image with the same length and width;
  • the appearance area of the back panel of the electronic device includes side areas other than the front screen area and the back area where the electronics are arranged.
  • the side areas are generally installed with earphone holes, speakers, charging holes and other components.
  • the image of the appearance area of the backplane has an abnormal aspect ratio, which is convenient for subsequent model recognition and avoids image loss. It is necessary to adjust the aspect ratio of the image of the appearance area of the backplane to 1:1;
  • the third device is used to input the adjusted backplane appearance area image into the model of the FPN network combined with the backbone network after the training;
  • the fourth device is used to receive and output the defect detection result of the backplane appearance area of the electronic device from the model of the FPN network combined with the backbone network.
  • the defect detection result includes: the type of the defect of the backplane of the electronic device and the defect in the electronic device. The position in the backplane of the device and the confidence level of the defect detection result.
  • each defect detection result of the backplane appearance area of the electronic device received and output from the FPN network combined with the backbone network model
  • each defect detection result includes cls, x1, y1, x2, y2, score, where cls is Defect type, x1, y1, x2, y2 are the 4 coordinates of the position of the defect in the image of the back panel appearance area, and score is the confidence level of this defect.
  • the present invention mainly utilizes the improved feature pyramid (FPN) network combined with the deep learning model of the backbone network to accurately identify the difference in appearance of the electronic device backplanes of second-hand electronic devices such as mobile phones.
  • FPN improved feature pyramid
  • the second device is used to extract the appearance area image of the backplane of the electronic device from the appearance image of the electronic device by using the Unet instance segmentation method .
  • the appearance area image of the backplane can be obtained quickly and efficiently.
  • the first two layers of the backbone network adopt a res structure
  • the last two layers of the network adopt an inception structure
  • the fourth device is also used to identify whether the confidence of the defect detection result is greater than a first preset threshold, and if it is greater than the first preset Threshold, then output the result information including the defect type of the backplane of the electronic device and the position of the defect on the backplane of the electronic device.
  • the types of defects on the backplane of the electronic device may sequentially include the types of shallow scratches, hard scratches, and chipping with increasing levels.
  • a fifth device including:
  • the fifth device is used to preset the FPN network combined with the backbone network model and its initial model parameters
  • the fifth and second device is used to input the image of the backplane appearance area of the sample electronic device into the FPN network with the current model parameters combined with the backbone network model to obtain the defect prediction result of the backplane of the sample electronic device, and the defect prediction result Including: the type of defect on the backplane of the sample electronic device, the position of the defect in the backplane of the sample electronic device, and the confidence level of the defect detection result;
  • the fifty-third device is configured to calculate the difference between the defect prediction result and the actual defect result of the sample electronic device based on a preset objective function, and to identify whether the difference is greater than a second preset threshold, and if the difference is Greater than the second preset threshold, execute the fifth and fourth device, which is used to update the model parameters of the FPN network combined with the backbone network based on the difference based on the fifth and fourth device, and then restart from the fifth and second device carried out;
  • the fifth and fifth device is executed, and the model of the FPN network combined with the backbone network with the current model parameters is used as the model of the FPN network combined with the backbone network after the training is completed.
  • the model of the FPN network combined with the backbone network is cyclically trained to obtain a reliable model.
  • the second device is used to scale the longitudinal direction of the image of the appearance area of the backplane.
  • the image of the appearance area of the backplane may be scaled to a size of 2048*2048 pixels, so as to obtain the image of the appearance area of the backplane adjusted to an image with the same length and width.
  • the present invention also provides a computing-based device, which includes:
  • a memory arranged to store computer-executable instructions which, when executed, cause the processor to:
  • Step S1 obtaining an appearance image of the electronic device
  • Step S2 extracting the appearance area image of the backboard of the electronic device from the appearance image of the electronic device, and adjusting the appearance area image of the backboard to an image with the same length and width;
  • Step S3 input the adjusted backplane appearance area image into the model of the FPN network after the training and the backbone network;
  • Step S4 receiving and outputting the defect detection result of the backplane appearance area of the electronic device from the model of the FPN network combined with the backbone network.
  • the defect detection result includes: the defect type of the backplane of the electronic device and the defect on the back of the electronic device. The position in the board and the confidence level of the defect detection result.
  • the present invention also provides a computer-readable storage medium on which computer-executable instructions are stored, wherein, when the computer-executable instructions are executed by a processor, the processor:
  • Step S1 obtaining an appearance image of the electronic device
  • Step S2 extracting the appearance area image of the backboard of the electronic device from the appearance image of the electronic device, and adjusting the appearance area image of the backboard to an image with the same length and width;
  • Step S3 input the adjusted backplane appearance area image into the model of the FPN network combined with the backbone network after the training;
  • Step S4 receiving and outputting the defect detection result of the backplane appearance area of the electronic device from the model of the FPN network combined with the backbone network.
  • the defect detection result includes: the defect type of the backplane of the electronic device and the defect on the back of the electronic device. The position in the board and the confidence level of the defect detection result.
  • the present invention can be implemented in software and/or a combination of software and hardware.
  • it can be implemented by an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device.
  • the software program of the present invention may be executed by a processor to realize the steps or functions described above.
  • the software program (including related data structure) of the present invention can be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive or a floppy disk and similar devices.
  • some steps or functions of the present invention may be implemented by hardware, for example, as a circuit that cooperates with a processor to execute each step or function.
  • a part of the present invention can be applied as a computer program product, such as a computer program instruction, when it is executed by a computer, through the operation of the computer, the method and/or technical solution according to the present invention can be invoked or provided.
  • the program instructions for invoking the method of the present invention may be stored in a fixed or removable recording medium, and/or transmitted through a data stream in a broadcast or other signal-bearing medium, and/or stored in accordance with the Said program instructions run in the working memory of the computer equipment.
  • an embodiment according to the present invention includes a device including a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, trigger
  • the operation of the device is based on the aforementioned methods and/or technical solutions according to multiple embodiments of the present invention.

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PCT/CN2020/120878 2019-10-28 2020-10-14 电子设备背板外观瑕疵检测方法及设备 WO2021082921A1 (zh)

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